title:
On a functional equation appearing in characterization of distributions
  by the optimality of an estimate

abstract:
  Let $X$ be a second countable locally compact Abelian group containing no
subgroup topologically isomorphic to the circle group $\mathbb{T}$. Let $\mu$
be a probability distribution on $X$ such that its characteristic function
$\hat\mu(y)$ does not vanish and $\hat\mu(y)$ for some $n \geq 3$ satisfies the
equation $$ \prod_{j=1}^{n} \hat\mu(y_j + y) = \prod_{j=1}^{n}\hat\mu(y_j - y),
\quad \sum_{j=1}^{n} y_j = 0, \quad y_1,\dots,y_n,y \in Y. $$ Then $\mu$ is a
convolution of a Gaussian distribution and a distribution supported in the
subgroup of $X$ generated by elements of order 2.


title:
Some results on injectivity and multistationarity in chemical reaction
  networks

abstract:
  The goal of this paper is to gather and develop some necessary and sufficient
criteria for injectivity and multistationarity in vector fields associated with
a chemical reaction network under a variety of more or less general assumptions
on the nature of the network and the reaction rates. The results are primarily
linear algebraic or matrix-theoretic, with some graph-theoretic results also
mentioned. Several results appear in, or are close to, results in the
literature. Here, we emphasise the connections between the results, and where
possible, present elementary proofs which rely solely on basic linear algebra
and calculus. A number of examples are provided to illustrate the variety of
subtly different conclusions which can be reached via different computations.
In addition, many of the computations are implemented in a web-based open
source platform, allowing the reader to test examples including and beyond
those analysed in the paper.


title: The Multiple-orientability Thresholds for Random Hypergraphs

abstract:
  A $k$-uniform hypergraph $H = (V, E)$ is called $\ell$-orientable, if there
is an assignment of each edge $e\in E$ to one of its vertices $v\in e$ such
that no vertex is assigned more than $\ell$ edges. Let $H_{n,m,k}$ be a
hypergraph, drawn uniformly at random from the set of all $k$-uniform
hypergraphs with $n$ vertices and $m$ edges. In this paper we establish the
threshold for the $\ell$-orientability of $H_{n,m,k}$ for all $k\ge 3$ and
$\ell \ge 2$, i.e., we determine a critical quantity $c_{k, \ell}^*$ such that
with probability $1-o(1)$ the graph $H_{n,cn,k}$ has an $\ell$-orientation if
$c < c_{k, \ell}^*$, but fails doing so if $c > c_{k, \ell}^*$.
  Our result has various applications including sharp load thresholds for
cuckoo hashing, load balancing with guaranteed maximum load, and massive
parallel access to hard disk arrays.


title: PyECLOUD and build-up simulations at CERN

abstract:
  PyECLOUD is a newly developed code for the simulation of the electron cloud
(EC) build-up in particle accelerators. Almost entirely written in Python, it
is mostly based on the physical models already used in the ECLOUD code but,
thanks to the implementation of new optimized algorithms, it exhibits a
significantly improved performance in accuracy, speed, reliability and
flexibility. Such new features of PyECLOUD have been already broadly exploited
to study EC observations in the Large Hadron Collider (LHC) and its injector
chain as well as for the extrapolation to high luminosity upgrade scenarios.


title:
MOSE: optical turbulence and atmospherical parameters operational
  forecast at ESO ground-based sites. II: atmospherical parameters in the
  surface layer [0-30] m

abstract:
  This article is the second of a series of articles aiming at proving the
feasibility of the forecast of all the most relevant classical atmospherical
parameters for astronomical applications (wind speed and direction,
temperature, relative humidity) and the optical turbulence (Cn2 and the derived
astro-climatic parameters like seeing, isoplanatic angle, wavefront coherence
time...). This study is done in the framework of the MOSE project, and focused
above the two ESO ground-bases sites of Cerro Paranal and Cerro Armazones. In
this paper we present the results related to the Meso-Nh model ability in
reconstructing the surface layer atmospherical parameters (wind speed
intensity, wind direction and absolute temperature, [0-30] m a.g.l.). The model
reconstruction of all the atmospherical parameters in the surface layer is very
satisfactory. For the temperature, at all levels, the RMSE (Root Mean Square
Error) is inferior to 1{\deg}C. For the wind speed, it is ~2 m/s, and for the
wind direction, it is in the range [38-46{\deg}], at all levels, that
corresponds to a RMSE_relative in a range [21-26{\deg}]. If a filter is applied
for the wind direction (the winds inferior to 3 m/s are discarded from the
computations), the wind direction RMSE is in the range [30-41{\deg}], i.e. a
RMSE_relative in the range [17-23{\deg}]. The model operational forecast of the
surface layer atmospherical parameters is suitable for different applications,
among others: thermalization of the dome using the reconstructed temperature,
hours in advance, of the beginning the night; knowing in advance the main
direction which the strong winds will come from during the night could allow
the astronomer to anticipate the occurrence of a good/bad seeing night, and
plan the observations accordingly; preventing adaptive secondary mirrors shake
generated by the wind speed.


title:
MOSE: optical turbulence and atmospherical parameters operational
  forecast at ESO ground-based sites. I: Overview and atmospherical parameters
  vertical stratification on [0-20] km

abstract:
  We present the overview of the MOSE project (MOdeling ESO Sites) aiming at
proving the feasibility of the forecast of the classical atmospherical
parameters (wind speed intensity and direction, temperature, relative humidity)
and the optical turbulence OT (CN2 profiles and the most relevant integrated
astro-climatic parameters derived from the CN2: the seeing, the isoplanatic
angle, the wavefront coherence time) above the two ESO ground-based sites of
Cerro Paranal and Cerro Armazones. The final outcome of the study is to
investigate the opportunity to implement an automatic system for the forecast
of these parameters at these sites. In this paper we present results related to
the Meso-Nh model ability in reconstructing the vertical stratification of the
atmospherical parameters along the 20 km above the ground. The very
satisfactory performances shown by the model in reconstructing most of these
parameters (and in particular the wind speed) put this tool of investigation as
the most suitable to be used in astronomical observatories to support AO
facilities and to calculate the temporal evolution of the wind speed and the
wavefront coherence time at whatever temporal sampling. The further great
advantage of this solution is that such estimates can be available in advance
(order of some hours) with respect to the time of interest


title: Unimodality of the freely selfdecomposable probability laws

abstract:
  We show that any freely selfdecomposable probability law is unimodal. This is
the free probabilistic analog of Yamazato's result in [Ann. Probab. 6 (1978),
523-531].


title:
The triangle principle: a new approach to non-contextuality and local
  realism

abstract:
  In this paper we study an application of an information distance between two
measurements to the problem of non-contextuality and local realism. We
postulate the triangle principle which states that any information distance is
well defined on any pair of measurements, even if the two measurements cannot
be jointly performed. As a consequence, the triangle inequality for this
distance is obeyed for any three measurements. This simple principle is valid
in any classical realistic theory, however it does not hold in quantum theory.
It allows us to re-derive in an astonishingly simple way a large class of
non-contextuality and local realistic inequalities via multiple applications of
the triangle inequality. We also show that this principle can be applied to
derive monogamy relations. The triangle principle is different than the
assumption of non-contextuality and local realism, which is defined as a lack
of existence of a joined probability distribution giving rise to all measurable
data. Therefore, we show that one can design Bell-Kochen-Specker tests using a
different principle.


title:
Classification and Properties of Hyperconifold Singularities and
  Transitions

abstract:
  This paper is a detailed study of a class of isolated Gorenstein threefold
singularities, called hyperconifolds, that are finite quotients of the
conifold. First, it is shown that hyperconifold singularities arise naturally
in limits of smooth, compact Calabi--Yau threefolds (in particular), when the
group action on the covering space develops a fixed point. The
Z_n-hyperconifolds---those for which the quotient group is cyclic---are
classified, demonstrating a one-to-one correspondence between these
singularities and three-dimensional lens spaces L(n,k), which occur as the
vanishing cycles. The classification is constructive, and leads to a simple
proof that a Z_n-hyperconifold is mirror to an n-nodal variety. It is then
argued that all factorial Z_n-hyperconifolds have crepant, projective
resolutions, and this gives rise to transitions between smooth compact
Calabi--Yau threefolds, which are mirror to certain conifold transitions.
Formulae are derived for the change in both fundamental group and Hodge numbers
under such hyperconifold transitions.
  Finally, a number of explicit examples are given, to illustrate how to
construct new Calabi--Yau manifolds using hyperconifold transitions, and also
to highlight the differences which can occur when these singularities occur in
non-factorial varieties.


title: Causal Discovery with Continuous Additive Noise Models

abstract:
  We consider the problem of learning causal directed acyclic graphs from an
observational joint distribution. One can use these graphs to predict the
outcome of interventional experiments, from which data are often not available.
We show that if the observational distribution follows a structural equation
model with an additive noise structure, the directed acyclic graph becomes
identifiable from the distribution under mild conditions. This constitutes an
interesting alternative to traditional methods that assume faithfulness and
identify only the Markov equivalence class of the graph, thus leaving some
edges undirected. We provide practical algorithms for finitely many samples,
RESIT (Regression with Subsequent Independence Test) and two methods based on
an independence score. We prove that RESIT is correct in the population setting
and provide an empirical evaluation.


title: Weak integral conditions for BMO

abstract:
  We study the question of how much one can weaken the defining condition of
BMO. Specifically, we show that if $Q$ is a cube in $\mathbb{R}^n$ and
$h:[0,\infty)\to[0,\infty)$ is such that
$h(t)\underset{t\to\infty}{\longrightarrow}\infty,$ then
  $$ \sup_{J \text{subcube} Q} \frac1{|J|}\int_J h(|\varphi-\frac1{|J|}
\int_J\varphi |)<\infty \Longrightarrow \varphi\in BMO(Q). $$
  Under some additional assumptions on $h$ we obtain estimates on
$\|\varphi\|_{BMO}$ in terms of the supremum above. We also show that even
though the condition $h(t)\underset{t\to\infty}{\longrightarrow}\infty$ is not
necessary for this implication to hold, it becomes necessary if one considers
the dyadic BMO.


title:
Spatial distributions of core-collapse supernovae in infrared-bright
  galaxies

abstract:
  We have measured the correlation between the locations of core-collapse
supernovae (CCSNe) and host galaxy light in the Ha line, near ultraviolet
(NUV), R-band and Ks-band to constrain the progenitors of CCSNe using pixel
statistics. Our sample consists of 86 CCSNe in 57 infrared (IR)-bright
galaxies, of which many are starbursts and ten are luminous infrared galaxies
(LIRGs). We also analyse the radial distribution of CCSNe in these galaxies,
and determine power-law and exponential fits to CCSN surface density profiles.
To probe differences between the SNe of these galaxies and normal spiral
galaxies, our results were compared to previous studies with samples dominated
by normal spiral galaxies where possible. We obtained a normalised scale length
of 0.23^{+0.03}_{-0.02} R_25 for the CCSN surface density in IR-bright
galaxies; less than that derived for CCSNe in a sample dominated by normal
spiral galaxies (0.29 \pm 0.01). This reflects a more centrally concentrated
population of massive stars in IR-bright galaxies. Furthermore, this
centralisation is dominated by a central excess of type Ibc/IIb SNe. This may
be due to a top-heavy initial mass function and/or an enhanced close binary
fraction in regions of enhanced star formation. Type Ic SNe are most strongly
correlated with Ha light and NUV-bright regions, reflecting the shortest
lifetime and thus highest mass for type Ic progenitors. Previous studies with
samples dominated by normal spiral galaxies have indicated a lower Ibc-Ha
correlation than our results do, which may be due to the central excess of type
Ibc/IIb SNe in our sample. The difference between types II and Ib is minimal,
suggesting that progenitor mass is not the dominant factor in determining if a
SN is of type Ib or II. Similar differences in correlation can be seen in the
Ks-band, with type Ibc/IIb SNe tracing the Ks-band light better than type II in
our sample.


title: On Blow-up criterion for the Nonlinear Schr\"{o}dinger Equation

abstract:
  The blowup is studied for the nonlinear Schr\"{o}dinger equation
$iu_{t}+\Delta u+ |u|^{p-1}u=0$ with $p$ is odd and $p\ge 1+\frac 4{N-2}$ (the
energy-critical or energy-supercritical case). It is shown that the solution
with negative energy $E(u_0)<0$ blows up in finite or infinite time. A new
proof is also presented for the previous result in \cite{HoRo2}, in which a
similar result but more general in a case of energy-subcritical was shown.


title:
Beyond consistency test of gravity with redshift-space distortions at
  quasi-linear scales

abstract:
  Redshift-space distortions (RSD) offer an attractive method to measure the
growth of cosmic structure on large scales, and combining with the measurement
of the cosmic expansion history, it can be used as cosmological tests of
gravity. With the advent of future galaxy redshift surveys aiming at precisely
measuring the RSD, an accurate modeling of RSD going beyond linear theory is a
critical issue in order to detect or disprove small deviations from general
relativity (GR). While several improved models of RSD have been recently
proposed based on the perturbation theory (PT), the framework of these models
heavily relies on GR. Here, we put forward a new PT prescription for RSD in
general modified gravity models. As a specific application, we present
theoretical predictions of the redshift-space power spectra in f(R) gravity
model, and compare them with N-body simulations. Using the PT template that
takes into account the effects of both modifications of gravity and RSD
properly, we successfully recover the fiducial model parameter in N-body
simulations in an unbiased way. On the other hand, we found it difficult to
detect the scale dependence of the growth rate in a model-independent way based
on GR templates.


title:
An extended anyon Fock space and noncommutative Meixner-type orthogonal
  polynomials in infinite dimensions

abstract:
  Let $\nu$ be a finite measure on $\mathbb R$ whose Laplace transform is
analytic in a neighborhood of zero. An anyon L\'evy white noise on $(\mathbb
R^d,dx)$ is a certain family of noncommuting operators
$\langle\omega,\varphi\rangle$ in the anyon Fock space over $L^2(\mathbb
R^d\times\mathbb R,dx\otimes\nu)$. Here $\varphi=\varphi(x)$ runs over a space
of test functions on $\mathbb R^d$, while $\omega=\omega(x)$ is interpreted as
an operator-valued distribution on $\mathbb R^d$. Let $L^2(\tau)$ be the
noncommutative $L^2$-space generated by the algebra of polynomials in variables
$\langle \omega,\varphi\rangle$, where $\tau$ is the vacuum expectation state.
We construct noncommutative orthogonal polynomials in $L^2(\tau)$ of the form
$\langle P_n(\omega),f^{(n)}\rangle$, where $f^{(n)}$ is a test function on
$(\mathbb R^d)^n$. Using these orthogonal polynomials, we derive a unitary
isomorphism $U$ between $L^2(\tau)$ and an extended anyon Fock space over
$L^2(\mathbb R^d,dx)$, denoted by $\mathbf F(L^2(\mathbb R^d,dx))$. The usual
anyon
  Fock space over $L^2(\mathbb R^d,dx)$, denoted by $\mathcal F(L^2(\mathbb
R^d,dx))$, is a subspace of $\mathbf F(L^2(\mathbb R^d,dx))$. Furthermore, we
have the equality $\mathbf F(L^2(\mathbb R^d,dx))=\mathcal F(L^2(\mathbb
R^d,dx))$ if and only if the measure $\nu$ is concentrated at one point, i.e.,
in the Gaussian/Poisson case. Using the unitary isomorphism $U$, we realize the
operators $\langle \omega,\varphi\rangle$ as a Jacobi (i.e., tridiagonal) field
in $\mathbf F(L^2(\mathbb R^d,dx))$. We derive a Meixner-type class of anyon
L\'evy white noise for which the respective Jacobi field in $\mathbf
F(L^2(\mathbb R^d,dx))$ has a relatively simple structure.


title: Electronic Structure of Ordered Double Perovskite Ba2CoWO6

abstract:
  Ba2CoWO6 (BCoW) has been synthesized in polycrystalline form by solid state
reaction at 1200C. Structural characterization of the compound was done through
X-ray diffraction (XRD) followed by Rietveld analysis of the XRD pattern. The
crystal structure is cubic, space group Fm-3m (No 225) with the lattice
parameter, a=8.210A. Optical band-gap of the present system has been calculated
using the UV-Vis Spectroscopy and Kubelka-Munk function, its value being 2.45
eV. A detailed study of the electronic properties has also been carried out
using the density functional theory (DFT) techniques implemented on WIEN2k.
Importance of electron-electron interaction between the Co ions leading to
half-metallic behavior, crystal and exchange splitting together with the
hybridization between O and Co, W has been investigated using the total and
partial density of states.


title: One-class Collaborative Filtering with Random Graphs: Annotated Version

abstract:
  The bane of one-class collaborative filtering is interpreting and modelling
the latent signal from the missing class. In this paper we present a novel
Bayesian generative model for implicit collaborative filtering. It forms a core
component of the Xbox Live architecture, and unlike previous approaches,
delineates the odds of a user disliking an item from simply not considering it.
The latent signal is treated as an unobserved random graph connecting users
with items they might have encountered. We demonstrate how large-scale
distributed learning can be achieved through a combination of stochastic
gradient descent and mean field variational inference over random graph
samples. A fine-grained comparison is done against a state of the art baseline
on real world data.


title:
K3 surfaces with non-symplectic automorphisms of order three and
  Calabi-Yau orbifolds

abstract:
  Let S be a K3 surface that admits a non-symplectic automorphism $\rho$ of
order 3. We divide $S\times \mathbb{P}^1$ by $\rho\times\psi$ where $\psi$ is
an automorphism of order 3 of $\mathbb{P}^1$. There exists a threefold ramified
cover of a partial crepant resolution of the quotient that is a Calabi-Yau
orbifold. We compute the Euler characteristic of our examples and obtain values
ranging from 30 to 219.


title: Successive Interference Cancellation in Heterogeneous Cellular Networks

abstract:
  At present, operators address the explosive growth of mobile data demand by
densification of the cellular network so as to reduce the transmitter-receiver
distance and to achieve higher spectral efficiency. Due to such network
densification and the intense proliferation of wireless devices, modern
wireless networks are interference-limited, which motivates the use of
interference mitigation and coordination techniques. In this work, we develop a
statistical framework to evaluate the performance of multi-tier heterogeneous
networks with successive interference cancellation (SIC) capabilities,
accounting for the computational complexity of the cancellation scheme and
relevant network related parameters such as random location of the access
points (APs) and mobile users, and the characteristics of the wireless
propagation channel. We explicitly model the consecutive events of canceling
interferers and we derive the success probability to cancel the n-th strongest
signal and to decode the signal of interest after n cancellations. When users
are connected to the AP which provides the maximum average received signal
power, the analysis indicates that the performance gains of SIC diminish
quickly with n and the benefits are modest for realistic values of the
signal-to-interference ratio (SIR). We extend the statistical model to include
several association policies where distinct gains of SIC are expected: (i)
minimum load association, (ii) maxi- mum instantaneous SIR association, and
(iii) range expansion. Numerical results show the effectiveness of SIC for the
considered association policies. This work deepens the understanding of SIC by
defining the achievable gains for different association policies in multi-tier
heterogeneous networks.


title:
Pattern formation in a flux limited reaction-diffusion of porous media
  type

abstract:
  A nonlinear PDE featuring flux limitation effects together with those of the
porous media equation (nonlinear Fokker-Planck) is presented in this paper. We
analyze the balance of such diverse effects through the study of the existence
and qualitative behavior of some admissible patterns, namely traveling wave
solutions, to this singular reaction-difusion equation. We show the existence
and qualitative behavior of different types of traveling waves: classical
profiles for wave speeds high enough, and discontinuous waves that are
reminiscent of hyperbolic shock waves when the wave speed lowers below a
certain threshold. Some of these solutions are of particular relevance as they
provide models by which the whole solution (and not just the bulk of it, as it
is the case with classical traveling waves) spreads through the medium with
finite speed.


title: The potential and perils of preprocessing: Building new foundations

abstract:
  Preprocessing forms an oft-neglected foundation for a wide range of
statistical and scientific analyses. However, it is rife with subtleties and
pitfalls. Decisions made in preprocessing constrain all later analyses and are
typically irreversible. Hence, data analysis becomes a collaborative endeavor
by all parties involved in data collection, preprocessing and curation, and
downstream inference. Even if each party has done its best given the
information and resources available to them, the final result may still fall
short of the best possible in the traditional single-phase inference framework.
This is particularly relevant as we enter the era of "big data". The
technologies driving this data explosion are subject to complex new forms of
measurement error. Simultaneously, we are accumulating increasingly massive
databases of scientific analyses. As a result, preprocessing has become more
vital (and potentially more dangerous) than ever before.


title: Four variants of theory of the second order phase transitions

abstract:
  Because of one-valued connection between the configurational entropy and the
order parameter it is possible to present the theory of the second order phase
transitions in terms of the configurational entropy. It is offered a variant of
theory, in which the Nernst theorem is obeyed. Within the framework of
heterogeneous model the phenomena of growth of level of fluctuations and their
correlations are analyzed at transition of critical point as competitions of
kinetic and relaxation processes in the conditions of proximity of two critical
points.


title: The long helical jet of the Lighthouse nebula, IGR J11014-6103

abstract:
  Jets from rotation-powered pulsars have so far only been observed in systems
moving subsonically trough their ambient medium and/or embedded in their
progenitor supernova remnant (SNR). Supersonic runaway pulsars are also
expected to produce jets, but they have not been confirmed so far. We
investigated the nature of the jet-like structure associated to the INTEGRAL
source IGR J11014-6103 (the "Lighthouse nebula"). The source is a neutron star
escaping its parent SNR MSH 11-61A supersonically at a velocity exceeding 1000
km/s. We observed the Lighthouse nebula and its jet-like X-ray structure
through dedicated high spatial resolution observations in X-rays (Chandra) and
radio band (ATCA). Our results show that the feature is a true pulsar's jet. It
extends highly collimated over >11pc, displays a clear precession-like
modulation, and propagates nearly perpendicular to the system direction of
motion, implying that the neutron star's spin axis in IGR J11014-6103 is almost
perpendicular to the direction of the kick received during the supernova
explosion. Our findings suggest that jets are common to rotation-powered
pulsars, and demonstrate that supernovae can impart high kick velocities to
misaligned spinning neutron stars, possibly through distinct, exotic,
core-collapse mechanisms.


title: Effective interaction: From nuclear reactions to neutron stars

abstract:
  An equation of state (EoS) for symmetric nuclear matter is constructed using
the density dependent M3Y effective interaction and extended for isospin
asymmetric nuclear matter. Theoretically obtained values of symmetric nuclear
matter incompressibility, isobaric incompressibility, symmetry energy and its
slope agree well with experimentally extracted values. Folded microscopic
potentials using this effective interaction, whose density dependence is
determined from nuclear matter calculations, provide excellent descriptions for
proton, alpha and cluster radioactivities, elastic and inelastic scattering.
The nuclear deformation parameters extracted from inelastic scattering of
protons agree well with other available results. The high density behavior of
symmetric and asymmetric nuclear matter satisfies the constraints from the
observed flow data of heavy-ion collisions. The neutron star properties studied
using $\beta$-equilibrated neutron star matter obtained from this effective
interaction reconcile with the recent observations of the massive compact
stars.


title:
Lifshitz Field Theories at Non-Zero Temperature, Hydrodynamics and
  Gravity

abstract:
  We consider a covariant formulation of field theories with Lifshitz scaling,
and analyze the energy-momentum tensor and the scale symmetry Ward identity. We
derive the equation of state and the ideal Lifshitz hydrodynamics in agreement
with arXiv:1304.7481, where they were determined by using thermodynamics and
symmetry properties. We construct the charged ideal Lifshitz hydrodynamics in
the generating functional framework as well as in the gravitational holographic
dual description. At the first viscous order, an analysis of the entropy
current reveals two additional transport coefficients (one dissipative and one
dissipationless) compared to the neutral case, contributing to the charge
current and to the asymmetric part of the energy-momentum tensor.


title: Electron cloud in the CERN accelerators (PS, SPS, LHC)

abstract:
  Several indicators have pointed to the presence of an Electron Cloud (EC) in
some of the CERN accelerators, when operating with closely spaced bunched
beams. In particular, spurious signals on the pick ups used for beam detection,
pressure rise and beam instabilities were observed at the Proton Synchrotron
(PS) during the last stage of preparation of the beams for the Large Hadron
Collider (LHC), as well as at the Super Proton Synchrotron (SPS). Since the LHC
has started operation in 2009, typical electron cloud phenomena have appeared
also in this machine, when running with trains of closely packed bunches (i.e.
with spacings below 150ns). Beside the above mentioned indicators, other
typical signatures were seen in this machine (due to its operation mode and/or
more refined detection possibilities), like heat load in the cold dipoles,
bunch dependent emittance growth and degraded lifetime in store and
bunch-by-bunch stable phase shift to compensate for the energy loss due to the
electron cloud. An overview of the electron cloud status in the different CERN
machines (PS, SPS, LHC) will be presented in this paper, with a special
emphasis on the dangers for future operation with more intense beams and the
necessary countermeasures to mitigate or suppress the effect.


title: A measurable counterpart to the optical concept of an object

abstract:
  The traditional optical concept for the object does not provide an
experimental feasibility to speak for itself, due to the fact that no measuring
instrument catches up with the fluctuation of light fields. Using the theory of
coherence, we give it a measurable counterpart, and thus to give a satisfactory
explanation of what the concept has said. To our knowledge it is the first time
to valuate an object in optics in the terms of observable quantities. As a
useful example, the applicability to obtain the full knowledge of an object
under the partial coherent illuminations is suggested. This work hit the
thought which had been advanced by E. Wolf that one measures the correlation
function rather than the optical fields themselves. The suggested applicability
is a supplementary to the recently found solution to the determination of
phases of the diffracted beam.


title: Minimizing Rosenthal Potential in Multicast Games

abstract:
  A multicast game is a network design game modelling how selfish
non-cooperative agents build and maintain one-to-many network communication.
There is a special source node and a collection of agents located at
corresponding terminals. Each agent is interested in selecting a route from the
special source to its terminal minimizing the cost. The mutual influence of the
agents is determined by a cost sharing mechanism, which evenly splits the cost
of an edge among all the agents using it for routing. In this paper we provide
several algorithmic and complexity results on finding a Nash equilibrium
minimizing the value of Rosenthal potential. Let n be the number of agents and
G be the communication network. We show that
  - For a given strategy profile s and integer k>=1, there is a local search
algorithm which in time n^{O(k)}|G|^{O(1)} finds a better strategy profile, if
there is any, in a k-exchange neighbourhood of s. In other words, the algorithm
decides if Rosenthal potential can be decreased by changing strategies of at
most k agents;
  - The running time of our local search algorithm is essentially tight: unless
FPT= W[1], for any function f(k), searching of the k-neighbourhood cannot be
done in time f(k)|G|^{O(1)}.
  The key ingredient of our algorithmic result is a subroutine that finds an
equilibrium with minimum potential in 3^n|G|^{O(1)} time. In other words,
finding an equilibrium with minimum potential is fixed-parameter tractable when
parameterized by the number of agents.


title: Inequalities for a number of different types of convexity

abstract: Some inequalities for different types of convexity are established.


title: Divergent CR-Equivalences and Meromorphic Differential Equations

abstract:
  Using the analytic theory of differential equations, we construct examples of
formally but not holomorphically equivalent real-analytic Levi nonflat
hypersurfaces in $\CC{n}$ together with examples of such hypersurfaces with
divergent formal CR-automorphisms.


title:
Goal oriented adaptivity in the IRGNM for parameter identification in
  PDEs I:\\ reduced formulation

abstract:
  In this paper we study adaptive discretization of the iteratively regularized
Gauss-Newton method IRGNM with an a posteriori (discrepancy principle) choice
of the regularization parameter in each Newton step and of the stopping index.
We first of all prove convergence and convergence rates under some accuracy
requirements formulated in terms of four quantities of interest. Then
computation of error estimators for these quantities based on a weighted dual
residual method is discussed, which results in an algorithm for adaptive
refinement.
  Finally we extend the results from the Hilbert space setting with quadratic
penalty to Banach spaces and general
  Tikhonov functionals for the regularization of each Newton step.


title: A combinatorial proof for Cayley's identity

abstract:
  In a recent paper, Caracciolo, Sokal and Sportiello presented, inter alia, an
algebraic/combinatorial proof for Cayley's identity. The purpose of the present
paper is to give a "purely combinatorial" proof for this identity; i.e., a
proof involving only combinatorial arguments together with a generalization of
Laplace's Theorem, for which a "purely combinatorial" proof is already known.


title: Azimuthal dynamo wave in spherical shell convection

abstract:
  We report the finding of an azimuthal dynamo wave of a low-order (m=1) mode
in direct numerical simulations (DNS) of turbulent convection in spherical
shells. Such waves are predicted by mean field dynamo theory and have been
obtained previously in mean-field models. Observational results both from
photometry and Doppler imaging have revealed persistent drifts of spots for
several rapidly rotating stars, but, although an azimuthal dynamo wave has been
proposed as a possible mechanism responsible for this behavior, it has been
judged as unlikely, as practical evidence for such waves from DNS has been
lacking. The large-scale magnetic field in our DNS, which is due to
self-consistent dynamo action, is dominated by a retrograde m=1 mode. Its
pattern speed is nearly independent of latitude and does not reflect the speed
of the differential rotation at any depth. The extrema of magnetic m=1
structures coincide reasonably with the maxima of m=2 structures of the
temperature. These results provide direct support for the observed drifts being
due to an azimuthal dynamo wave.


title: On supersymmetric Chern-Simons-type theories in five dimensions

abstract:
  We present a closed-form expression for the supersymmetric non-Abelian
Chern-Simons action in conventional five-dimensional N=1 superspace. Our
construction makes use of the superform formalism to generate supersymmetric
invariants. Similar ideas are applied to construct supersymmetric actions for
off-shell supermultiplets with an intrinsic central charge. In particular, the
large tensor multiplet is described in superspace for the first time.


title:
The polluted atmosphere of the white dwarf NLTT 25792 and the diversity
  of circumstellar environments

abstract:
  We present an analysis of X-Shooter spectra of the polluted, hydrogen-rich
white dwarf NLTT 25792. The spectra show strong lines of calcium (Ca H&K,
near-infrared calcium triplet, and Ca I 4226 and numerous lines of iron along
with magnesium and aluminum lines from which we draw the abundance pattern.
Moreover, the photospheric Ca H&K lines are possibly blended with a
circumstellar component shifted by -20 km/s relative to the photosphere. A
comparison with a sample of four white dwarfs with similar parameters show
considerable variations in their abundance patterns, particularly in the
calcium to magnesium abundance ratio that varies by a factor of five within
this sample. The observed variations, even after accounting for diffusion
effects, imply similar variations in the putative accretion source. Also, we
find that silicon and sodium are significantly underabundant in the atmosphere
of NLTT 25792, a fact that may offer some clues on the nature of the accretion
source.


title:
White paper: CeLAND - Investigation of the reactor antineutrino anomaly
  with an intense 144Ce-144Pr antineutrino source in KamLAND

abstract:
  We propose to test for short baseline neutrino oscillations, implied by the
recent reevaluation of the reactor antineutrino flux and by anomalous results
from the gallium solar neutrino detectors. The test will consist of producing a
75 kCi 144Ce - 144Pr antineutrino source to be deployed in the Kamioka Liquid
Scintillator Anti-Neutrino Detector (KamLAND). KamLAND's 13m diameter target
volume provides a suitable environment to measure energy and position
dependence of the detected neutrino flux. A characteristic oscillation pattern
would be visible for a baseline of about 10 m or less, providing a very clean
signal of neutrino disappearance into a yet-unknown, "sterile" state. Such a
measurement will be free of any reactor-related uncertainties. After 1.5 years
of data taking the Reactor Antineutrino Anomaly parameter space will be tested
at > 95% C.L.


title: Blind pilot decontamination

abstract:
  A subspace projection to improve channel estimation in massive multi-antenna
systems is proposed and analyzed. Together with power-controlled hand-off, it
can mitigate the pilot contamination problem without the need for coordination
among cells. The proposed method is blind in the sense that it does not require
pilot data to find the appropriate subspace. It is based on the theory of large
random matrices that predicts that the eigenvalue spectra of large sample
covariance matrices can asymptotically decompose into disjoint bulks as the
matrix size grows large. Random matrix and free probability theory are utilized
to predict under which system parameters such a bulk decomposition takes place.
Simulation results are provided to confirm that the proposed method outperforms
conventional linear channel estimation if bulk separation occurs.


title:
Long-Range triplet Josephson Current Modulated by the Interface
  Magnetization Texture

abstract:
  We have investigated the Josephson coupling between two s-wave
superconductors separated by the ferromagnetic trilayers with noncollinear
magnetization. We find that the long-range triplet critical current will
oscillate with the strength of the exchange field and the thickness of the
interface layer, when the interface magnetizations are orthogonal to the
central magnetization. This feature is induced by the spatial oscillations of
the spin-triplet state |\uparrow\downarrow>+|\downarrow\uparrow> in the
interface layer. Moreover, the critical current can exhibit a characteristic
nonmonotonic behavior, when the misalignment angle between interface
magnetization and central ferromagnet increases from 0 to \pi/2. This peculiar
behavior will take place under the condition that the original state of the
junction with the parallel magnetizations is the \pi state.


title: Using river locks to teach hydrodynamic concepts

abstract:
  In this work, the use of a river lock as a non-formal setting for teaching Q2
hydrodynamical concepts is proposed. In particular, we describe the operation
of a river lock situated at the Sobradinho dam, on the S\~ao Francisco River
(Brazil). A model to represent and to analyse the dynamics of river lock
operation is presented and we derive the dynamical equations for the rising of
the water column as an example to understand the Euler equation. Furthermore,
with this activity, we enable the integration of content initially introduced
in the classroom with practical applications, thereby allowing the association
of physical themes to content relevant in disciplines such as history and
geography. In addition, experiences of this kind enable teachers to talk about
the environmental and social impacts caused by the construction of a dam and,
consequently, a crossover of concepts has been made possible, leading to more
meaningful learning for the students.


title: Free volume distribution of nearly jammed hard sphere packings

abstract:
  We calculate the free volume distributions of nearly jammed packings of
monodisperse and bidisperse hard sphere configurations. These distributions
differ qualitatively from those of the fluid, displaying a power law tail at
large free volumes, which constitutes a distinct signature of nearly jammed
configurations, persisting for moderate degrees of decompression. We reproduce
and explain the observed distribution by considering the pair correlation
function within the first coordination shell for jammed hard sphere
configurations. We analyze features of the equation of state near jamming, and
discuss the significance of observed asphericities of the free volumes to the
equation of state.


title:
On the regularity of critical and minimal sets of a free interface
  problem

abstract:
  We study a free interface problem of finding the optimal energy configuration
for mixtures of two conducting materials with an additional perimeter
penalization of the interface. We employ the regularity theory of linear
elliptic equations to study the possible opening angles of Taylor cones and to
give a different proof of a partial regularity result by Fan Hua Lin.


title:
Generative Multiple-Instance Learning Models For Quantitative
  Electromyography

abstract:
  We present a comprehensive study of the use of generative modeling approaches
for Multiple-Instance Learning (MIL) problems. In MIL a learner receives
training instances grouped together into bags with labels for the bags only
(which might not be correct for the comprised instances). Our work was
motivated by the task of facilitating the diagnosis of neuromuscular disorders
using sets of motor unit potential trains (MUPTs) detected within a muscle
which can be cast as a MIL problem. Our approach leads to a state-of-the-art
solution to the problem of muscle classification. By introducing and analyzing
generative models for MIL in a general framework and examining a variety of
model structures and components, our work also serves as a methodological guide
to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets
and on the MUSK1 dataset, one of the most widely used benchmarks for MIL.


title: The Bregman Variational Dual-Tree Framework

abstract:
  Graph-based methods provide a powerful tool set for many non-parametric
frameworks in Machine Learning. In general, the memory and computational
complexity of these methods is quadratic in the number of examples in the data
which makes them quickly infeasible for moderate to large scale datasets. A
significant effort to find more efficient solutions to the problem has been
made in the literature. One of the state-of-the-art methods that has been
recently introduced is the Variational Dual-Tree (VDT) framework. Despite some
of its unique features, VDT is currently restricted only to Euclidean spaces
where the Euclidean distance quantifies the similarity. In this paper, we
extend the VDT framework beyond the Euclidean distance to more general Bregman
divergences that include the Euclidean distance as a special case. By
exploiting the properties of the general Bregman divergence, we show how the
new framework can maintain all the pivotal features of the VDT framework and
yet significantly improve its performance in non-Euclidean domains. We apply
the proposed framework to different text categorization problems and
demonstrate its benefits over the original VDT.


title:
Hinge-loss Markov Random Fields: Convex Inference for Structured
  Prediction

abstract:
  Graphical models for structured domains are powerful tools, but the
computational complexities of combinatorial prediction spaces can force
restrictions on models, or require approximate inference in order to be
tractable. Instead of working in a combinatorial space, we use hinge-loss
Markov random fields (HL-MRFs), an expressive class of graphical models with
log-concave density functions over continuous variables, which can represent
confidences in discrete predictions. This paper demonstrates that HL-MRFs are
general tools for fast and accurate structured prediction. We introduce the
first inference algorithm that is both scalable and applicable to the full
class of HL-MRFs, and show how to train HL-MRFs with several learning
algorithms. Our experiments show that HL-MRFs match or surpass the predictive
performance of state-of-the-art methods, including discrete models, in four
application domains.


title:
High-dimensional Joint Sparsity Random Effects Model for Multi-task
  Learning

abstract:
  Joint sparsity regularization in multi-task learning has attracted much
attention in recent years. The traditional convex formulation employs the group
Lasso relaxation to achieve joint sparsity across tasks. Although this approach
leads to a simple convex formulation, it suffers from several issues due to the
looseness of the relaxation. To remedy this problem, we view jointly sparse
multi-task learning as a specialized random effects model, and derive a convex
relaxation approach that involves two steps. The first step learns the
covariance matrix of the coefficients using a convex formulation which we refer
to as sparse covariance coding; the second step solves a ridge regression
problem with a sparse quadratic regularizer based on the covariance matrix
obtained in the first step. It is shown that this approach produces an
asymptotically optimal quadratic regularizer in the multitask learning setting
when the number of tasks approaches infinity. Experimental results demonstrate
that the convex formulation obtained via the proposed model significantly
outperforms group Lasso (and related multi-stage formulations


title:
Lower Bounds for Exact Model Counting and Applications in Probabilistic
  Databases

abstract:
  The best current methods for exactly computing the number of satisfying
assignments, or the satisfying probability, of Boolean formulas can be seen,
either directly or indirectly, as building 'decision-DNNF' (decision
decomposable negation normal form) representations of the input Boolean
formulas. Decision-DNNFs are a special case of 'd-DNNF's where 'd' stands for
'deterministic'. We show that any decision-DNNF can be converted into an
equivalent 'FBDD' (free binary decision diagram) -- also known as a 'read-once
branching program' (ROBP or 1-BP) -- with only a quasipolynomial increase in
representation size in general, and with only a polynomial increase in size in
the special case of monotone k-DNF formulas. Leveraging known exponential lower
bounds for FBDDs, we then obtain similar exponential lower bounds for
decision-DNNFs which provide lower bounds for the recent algorithms. We also
separate the power of decision-DNNFs from d-DNNFs and a generalization of
decision-DNNFs known as AND-FBDDs. Finally we show how these imply exponential
lower bounds for natural problems associated with probabilistic databases.


title: Reasoning about Probabilities in Dynamic Systems using Goal Regression

abstract:
  Reasoning about degrees of belief in uncertain dynamic worlds is fundamental
to many applications, such as robotics and planning, where actions modify state
properties and sensors provide measurements, both of which are prone to noise.
With the exception of limited cases such as Gaussian processes over linear
phenomena, belief state evolution can be complex and hard to reason with in a
general way. This paper proposes a framework with new results that allows the
reduction of subjective probabilities after sensing and acting to questions
about the initial state only. We build on an expressive probabilistic
first-order logical account by Bacchus, Halpern and Levesque, resulting in a
methodology that, in principle, can be coupled with a variety of existing
inference solutions.


title: Probabilistic Conditional Preference Networks

abstract:
  In order to represent the preferences of a group of individuals, we introduce
Probabilistic CP-nets (PCP-nets). PCP-nets provide a compact language for
representing probability distributions over preference orderings. We argue that
they are useful for aggregating preferences or modelling noisy preferences.
Then we give efficient algorithms for the main reasoning problems, namely for
computing the probability that a given outcome is preferred to another one, and
the probability that a given outcome is optimal. As a by-product, we obtain an
unexpected linear-time algorithm for checking dominance in a standard,
tree-structured CP-net.


title: Boosting in the presence of label noise

abstract:
  Boosting is known to be sensitive to label noise. We studied two approaches
to improve AdaBoost's robustness against labelling errors. One is to employ a
label-noise robust classifier as a base learner, while the other is to modify
the AdaBoost algorithm to be more robust. Empirical evaluation shows that a
committee of robust classifiers, although converges faster than non label-noise
aware AdaBoost, is still susceptible to label noise. However, pairing it with
the new robust Boosting algorithm we propose here results in a more resilient
algorithm under mislabelling.


title: Hilbert Space Embeddings of Predictive State Representations

abstract:
  Predictive State Representations (PSRs) are an expressive class of models for
controlled stochastic processes. PSRs represent state as a set of predictions
of future observable events. Because PSRs are defined entirely in terms of
observable data, statistically consistent estimates of PSR parameters can be
learned efficiently by manipulating moments of observed training data. Most
learning algorithms for PSRs have assumed that actions and observations are
finite with low cardinality. In this paper, we generalize PSRs to infinite sets
of observations and actions, using the recent concept of Hilbert space
embeddings of distributions. The essence is to represent the state as a
nonparametric conditional embedding operator in a Reproducing Kernel Hilbert
Space (RKHS) and leverage recent work in kernel methods to estimate, predict,
and update the representation. We show that these Hilbert space embeddings of
PSRs are able to gracefully handle continuous actions and observations, and
that our learned models outperform competing system identification algorithms
on several prediction benchmarks.


title:
SparsityBoost: A New Scoring Function for Learning Bayesian Network
  Structure

abstract:
  We give a new consistent scoring function for structure learning of Bayesian
networks. In contrast to traditional approaches to scorebased structure
learning, such as BDeu or MDL, the complexity penalty that we propose is
data-dependent and is given by the probability that a conditional independence
test correctly shows that an edge cannot exist. What really distinguishes this
new scoring function from earlier work is that it has the property of becoming
computationally easier to maximize as the amount of data increases. We prove a
polynomial sample complexity result, showing that maximizing this score is
guaranteed to correctly learn a structure with no false edges and a
distribution close to the generating distribution, whenever there exists a
Bayesian network which is a perfect map for the data generating distribution.
Although the new score can be used with any search algorithm, we give empirical
results showing that it is particularly effective when used together with a
linear programming relaxation approach to Bayesian network structure learning.


title: Sample Complexity of Multi-task Reinforcement Learning

abstract:
  Transferring knowledge across a sequence of reinforcement-learning tasks is
challenging, and has a number of important applications. Though there is
encouraging empirical evidence that transfer can improve performance in
subsequent reinforcement-learning tasks, there has been very little theoretical
analysis. In this paper, we introduce a new multi-task algorithm for a sequence
of reinforcement-learning tasks when each task is sampled independently from
(an unknown) distribution over a finite set of Markov decision processes whose
parameters are initially unknown. For this setting, we prove under certain
assumptions that the per-task sample complexity of exploration is reduced
significantly due to transfer compared to standard single-task algorithms. Our
multi-task algorithm also has the desired characteristic that it is guaranteed
not to exhibit negative transfer: in the worst case its per-task sample
complexity is comparable to the corresponding single-task algorithm.


title: Automorphism Groups of Graphical Models and Lifted Variational Inference

abstract:
  Using the theory of group action, we first introduce the concept of the
automorphism group of an exponential family or a graphical model, thus
formalizing the general notion of symmetry of a probabilistic model. This
automorphism group provides a precise mathematical framework for lifted
inference in the general exponential family. Its group action partitions the
set of random variables and feature functions into equivalent classes (called
orbits) having identical marginals and expectations. Then the inference problem
is effectively reduced to that of computing marginals or expectations for each
class, thus avoiding the need to deal with each individual variable or feature.
We demonstrate the usefulness of this general framework in lifting two classes
of variational approximation for maximum a posteriori (MAP) inference: local
linear programming (LP) relaxation and local LP relaxation with cycle
constraints; the latter yields the first lifted variational inference algorithm
that operates on a bound tighter than the local constraints.


title: Convex Relaxations of Bregman Divergence Clustering

abstract:
  Although many convex relaxations of clustering have been proposed in the past
decade, current formulations remain restricted to spherical Gaussian or
discriminative models and are susceptible to imbalanced clusters. To address
these shortcomings, we propose a new class of convex relaxations that can be
flexibly applied to more general forms of Bregman divergence clustering. By
basing these new formulations on normalized equivalence relations we retain
additional control on relaxation quality, which allows improvement in
clustering quality. We furthermore develop optimization methods that improve
scalability by exploiting recent implicit matrix norm methods. In practice, we
find that the new formulations are able to efficiently produce tighter
clusterings that improve the accuracy of state of the art methods.


title: Learning Sparse Causal Models is not NP-hard

abstract:
  This paper shows that causal model discovery is not an NP-hard problem, in
the sense that for sparse graphs bounded by node degree k the sound and
complete causal model can be obtained in worst case order N^{2(k+2)}
independence tests, even when latent variables and selection bias may be
present. We present a modification of the well-known FCI algorithm that
implements the method for an independence oracle, and suggest improvements for
sample/real-world data versions. It does not contradict any known hardness
results, and does not solve an NP-hard problem: it just proves that sparse
causal discovery is perhaps more complicated, but not as hard as learning
minimal Bayesian networks.


title: Advances in Bayesian Network Learning using Integer Programming

abstract:
  We consider the problem of learning Bayesian networks (BNs) from complete
discrete data. This problem of discrete optimisation is formulated as an
integer program (IP). We describe the various steps we have taken to allow
efficient solving of this IP. These are (i) efficient search for cutting
planes, (ii) a fast greedy algorithm to find high-scoring (perhaps not optimal)
BNs and (iii) tightening the linear relaxation of the IP. After relating this
BN learning problem to set covering and the multidimensional 0-1 knapsack
problem, we present our empirical results. These show improvements, sometimes
dramatic, over earlier results.


title: Qualitative Possibilistic Mixed-Observable MDPs

abstract:
  Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs
used to model situations where the agent's initial belief or observation
probabilities are imprecise due to lack of past experiences or insufficient
data collection. However, like probabilistic POMDPs, optimally solving
pi-POMDPs is intractable: the finite belief state space exponentially grows
with the number of system's states. In this paper, a possibilistic version of
Mixed-Observable MDPs is presented to get around this issue: the complexity of
solving pi-POMDPs, some state variables of which are fully observable, can be
then dramatically reduced. A value iteration algorithm for this new formulation
under infinite horizon is next proposed and the optimality of the returned
policy (for a specified criterion) is shown assuming the existence of a "stay"
action in some goal states. Experimental work finally shows that this
possibilistic model outperforms probabilistic POMDPs commonly used in robotics,
for a target recognition problem where the agent's observations are imprecise.


title:
Optimization With Parity Constraints: From Binary Codes to Discrete
  Integration

abstract:
  Many probabilistic inference tasks involve summations over exponentially
large sets. Recently, it has been shown that these problems can be reduced to
solving a polynomial number of MAP inference queries for a model augmented with
randomly generated parity constraints. By exploiting a connection with
max-likelihood decoding of binary codes, we show that these optimizations are
computationally hard. Inspired by iterative message passing decoding
algorithms, we propose an Integer Linear Programming (ILP) formulation for the
problem, enhanced with new sparsification techniques to improve decoding
performance. By solving the ILP through a sequence of LP relaxations, we get
both lower and upper bounds on the partition function, which hold with high
probability and are much tighter than those obtained with variational methods.


title:
Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical
  Efficiency

abstract:
  Popular Monte-Carlo tree search (MCTS) algorithms for online planning, such
as epsilon-greedy tree search and UCT, aim at rapidly identifying a reasonably
good action, but provide rather poor worst-case guarantees on performance
improvement over time. In contrast, a recently introduced MCTS algorithm BRUE
guarantees exponential-rate improvement over time, yet it is not geared towards
identifying reasonably good choices right at the go. We take a stand on the
individual strengths of these two classes of algorithms, and show how they can
be effectively connected. We then rationalize a principle of "selective tree
expansion", and suggest a concrete implementation of this principle within
MCTS. The resulting algorithm,s favorably compete with other MCTS algorithms
under short planning times, while preserving the attractive convergence
properties of BRUE.


title: Bethe-ADMM for Tree Decomposition based Parallel MAP Inference

abstract:
  We consider the problem of maximum a posteriori (MAP) inference in discrete
graphical models. We present a parallel MAP inference algorithm called
Bethe-ADMM based on two ideas: tree-decomposition of the graph and the
alternating direction method of multipliers (ADMM). However, unlike the
standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based
proximal function, which makes each subproblem in ADMM easy to solve in
parallel using the sum-product algorithm. We rigorously prove global
convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on
both synthetic and real datasets to illustrate its effectiveness. Further, the
parallel Bethe-ADMM is shown to scale almost linearly with increasing number of
cores.


title:
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit
  Lens

abstract:
  In this paper we propose a multi-armed bandit inspired, pool based active
learning algorithm for the problem of binary classification. By carefully
constructing an analogy between active learning and multi-armed bandits, we
utilize ideas such as lower confidence bounds, and self-concordant
regularization from the multi-armed bandit literature to design our proposed
algorithm. Our algorithm is a sequential algorithm, which in each round assigns
a sampling distribution on the pool, samples one point from this distribution,
and queries the oracle for the label of this sampled point. The design of this
sampling distribution is also inspired by the analogy between active learning
and multi-armed bandits. We show how to derive lower confidence bounds required
by our algorithm. Experimental comparisons to previously proposed active
learning algorithms show superior performance on some standard UCI datasets.


title: Batch-iFDD for Representation Expansion in Large MDPs

abstract:
  Matching pursuit (MP) methods are a promising class of feature construction
algorithms for value function approximation. Yet existing MP methods require
creating a pool of potential features, mandating expert knowledge or
enumeration of a large feature pool, both of which hinder scalability. This
paper introduces batch incremental feature dependency discovery (Batch-iFDD) as
an MP method that inherits a provable convergence property. Additionally,
Batch-iFDD does not require a large pool of features, leading to lower
computational complexity. Empirical policy evaluation results across three
domains with up to one million states highlight the scalability of Batch-iFDD
over the previous state of the art MP algorithm.


title: Structured Message Passing

abstract:
  In this paper, we present structured message passing (SMP), a unifying
framework for approximate inference algorithms that take advantage of
structured representations such as algebraic decision diagrams and sparse hash
tables. These representations can yield significant time and space savings over
the conventional tabular representation when the message has several identical
values (context-specific independence) or zeros (determinism) or both in its
range. Therefore, in order to fully exploit the power of structured
representations, we propose to artificially introduce context-specific
independence and determinism in the messages. This yields a new class of
powerful approximate inference algorithms which includes popular algorithms
such as cluster-graph Belief propagation (BP), expectation propagation and
particle BP as special cases. We show that our new algorithms introduce several
interesting bias-variance trade-offs. We evaluate these trade-offs empirically
and demonstrate that our new algorithms are more accurate and scalable than
state-of-the-art techniques.


title: Multiple Instance Learning by Discriminative Training of Markov Networks

abstract:
  We introduce a graphical framework for multiple instance learning (MIL) based
on Markov networks. This framework can be used to model the traditional MIL
definition as well as more general MIL definitions. Different levels of
ambiguity -- the portion of positive instances in a bag -- can be explored in
weakly supervised data. To train these models, we propose a discriminative
max-margin learning algorithm leveraging efficient inference for
cardinality-based cliques. The efficacy of the proposed framework is evaluated
on a variety of data sets. Experimental results verify that encoding or
learning the degree of ambiguity can improve classification performance.


title: Unsupervised Learning of Noisy-Or Bayesian Networks

abstract:
  This paper considers the problem of learning the parameters in Bayesian
networks of discrete variables with known structure and hidden variables.
Previous approaches in these settings typically use expectation maximization;
when the network has high treewidth, the required expectations might be
approximated using Monte Carlo or variational methods. We show how to avoid
inference altogether during learning by giving a polynomial-time algorithm
based on the method-of-moments, building upon recent work on learning
discrete-valued mixture models. In particular, we show how to learn the
parameters for a family of bipartite noisy-or Bayesian networks. In our
experimental results, we demonstrate an application of our algorithm to
learning QMR-DT, a large Bayesian network used for medical diagnosis. We show
that it is possible to fully learn the parameters of QMR-DT even when only the
findings are observed in the training data (ground truth diseases unknown).


title: Gaussian Processes for Big Data

abstract:
  We introduce stochastic variational inference for Gaussian process models.
This enables the application of Gaussian process (GP) models to data sets
containing millions of data points. We show how GPs can be vari- ationally
decomposed to depend on a set of globally relevant inducing variables which
factorize the model in the necessary manner to perform variational inference.
Our ap- proach is readily extended to models with non-Gaussian likelihoods and
latent variable models based around Gaussian processes. We demonstrate the
approach on a simple toy problem and two real world data sets.


title:
Discovering Cyclic Causal Models with Latent Variables: A General
  SAT-Based Procedure

abstract:
  We present a very general approach to learning the structure of causal models
based on d-separation constraints, obtained from any given set of overlapping
passive observational or experimental data sets. The procedure allows for both
directed cycles (feedback loops) and the presence of latent variables. Our
approach is based on a logical representation of causal pathways, which permits
the integration of quite general background knowledge, and inference is
performed using a Boolean satisfiability (SAT) solver. The procedure is
complete in that it exhausts the available information on whether any given
edge can be determined to be present or absent, and returns "unknown"
otherwise. Many existing constraint-based causal discovery algorithms can be
seen as special cases, tailored to circumstances in which one or more
restricting assumptions apply. Simulations illustrate the effect of these
assumptions on discovery and how the present algorithm scales.


title: Experimental realisation of a delayed-choice quantum walk

abstract:
  Many paradoxes of quantum mechanics come from the fact that a quantum system
can possess different features at the same time, such as in wave-particle
duality or quantum superposition. In recent delayed-choice experiments, a
quantum mechanical system can be observed to manifest one feature such as the
wave or particle nature, depending on the final measurement setup, which is
chosen after the system itself has already entered the measuring device; hence
its behaviour is not predetermined. Here, we adapt this paradigmatic scheme to
multi-dimensional quantum walks. In our experiment, the way in which a photon
interferes with itself in a strongly non-trivial pattern depends on its
polarisation, that is determined after the photon has already been detected.
Multi-dimensional quantum walks are a very powerful tool for simulating the
behaviour of complex quantum systems, due to their versatility. This is the
first experiment realising a multi-dimensional quantum walk with a
single-photon source and we present also the first experimental simulation of
the Grover walk, a model that can be used to implement the Grover quantum
search algorithm.


title:
Inverse Covariance Estimation for High-Dimensional Data in Linear Time
  and Space: Spectral Methods for Riccati and Sparse Models

abstract:
  We propose maximum likelihood estimation for learning Gaussian graphical
models with a Gaussian (ell_2^2) prior on the parameters. This is in contrast
to the commonly used Laplace (ell_1) prior for encouraging sparseness. We show
that our optimization problem leads to a Riccati matrix equation, which has a
closed form solution. We propose an efficient algorithm that performs a
singular value decomposition of the training data. Our algorithm is
O(NT^2)-time and O(NT)-space for N variables and T samples. Our method is
tailored to high-dimensional problems (N gg T), in which sparseness promoting
methods become intractable. Furthermore, instead of obtaining a single solution
for a specific regularization parameter, our algorithm finds the whole solution
path. We show that the method has logarithmic sample complexity under the
spiked covariance model. We also propose sparsification of the dense solution
with provable performance guarantees. We provide techniques for using our
learnt models, such as removing unimportant variables, computing likelihoods
and conditional distributions. Finally, we show promising results in several
gene expressions datasets.


title: Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search

abstract:
  A limited-memory influence diagram (LIMID) generalizes a traditional
influence diagram by relaxing the assumptions of regularity and no-forgetting,
allowing a wider range of decision problems to be modeled. Algorithms for
solving traditional influence diagrams are not easily generalized to solve
LIMIDs, however, and only recently have exact algorithms for solving LIMIDs
been developed. In this paper, we introduce an exact algorithm for solving
LIMIDs that is based on branch-and-bound search. Our approach is related to the
approach of solving an influence diagram by converting it to an equivalent
decision tree, with the difference that the LIMID is converted to a much
smaller decision graph that can be searched more efficiently.


title: Constrained Bayesian Inference for Low Rank Multitask Learning

abstract:
  We present a novel approach for constrained Bayesian inference. Unlike
current methods, our approach does not require convexity of the constraint set.
We reduce the constrained variational inference to a parametric optimization
over the feasible set of densities and propose a general recipe for such
problems. We apply the proposed constrained Bayesian inference approach to
multitask learning subject to rank constraints on the weight matrix. Further,
constrained parameter estimation is applied to recover the sparse conditional
independence structure encoded by prior precision matrices. Our approach is
motivated by reverse inference for high dimensional functional neuroimaging, a
domain where the high dimensionality and small number of examples requires the
use of constraints to ensure meaningful and effective models. For this
application, we propose a model that jointly learns a weight matrix and the
prior inverse covariance structure between different tasks. We present
experimental validation showing that the proposed approach outperforms strong
baseline models in terms of predictive performance and structure recovery.


title: Collective Diffusion Over Networks: Models and Inference

abstract:
  Diffusion processes in networks are increasingly used to model the spread of
information and social influence. In several applications in computational
sustainability such as the spread of wildlife, infectious diseases and traffic
mobility pattern, the observed data often consists of only aggregate
information. In this work, we present new models that generalize standard
diffusion processes to such collective settings. We also present optimization
based techniques that can accurately learn the underlying dynamics of the given
contagion process, including the hidden network structure, by only observing
the time a node becomes active and the associated aggregate information.
Empirically, our technique is highly robust and accurately learns network
structure with more than 90% recall and precision. Results on real-world flu
spread data in the US confirm that our technique can also accurately model
infectious disease spread.


title:
Causal Transportability of Experiments on Controllable Subsets of
  Variables: z-Transportability

abstract:
  We introduce z-transportability, the problem of estimating the causal effect
of a set of variables X on another set of variables Y in a target domain from
experiments on any subset of controllable variables Z where Z is an arbitrary
subset of observable variables V in a source domain. z-Transportability
generalizes z-identifiability, the problem of estimating in a given domain the
causal effect of X on Y from surrogate experiments on a set of variables Z such
that Z is disjoint from X;. z-Transportability also generalizes
transportability which requires that the causal effect of X on Y in the target
domain be estimable from experiments on any subset of all observable variables
in the source domain. We first generalize z-identifiability to allow cases
where Z is not necessarily disjoint from X. Then, we establish a necessary and
sufficient condition for z-transportability in terms of generalized
z-identifiability and transportability. We provide a correct and complete
algorithm that determines whether a causal effect is z-transportable; and if it
is, produces a transport formula, that is, a recipe for estimating the causal
effect of X on Y in the target domain using information elicited from the
results of experimental manipulations of Z in the source domain and
observational data from the target domain. Our results also show that
do-calculus is complete for z-transportability.


title:
A Sound and Complete Algorithm for Learning Causal Models from
  Relational Data

abstract:
  The PC algorithm learns maximally oriented causal Bayesian networks. However,
there is no equivalent complete algorithm for learning the structure of
relational models, a more expressive generalization of Bayesian networks.
Recent developments in the theory and representation of relational models
support lifted reasoning about conditional independence. This enables a
powerful constraint for orienting bivariate dependencies and forms the basis of
a new algorithm for learning structure. We present the relational causal
discovery (RCD) algorithm that learns causal relational models. We prove that
RCD is sound and complete, and we present empirical results that demonstrate
effectiveness.


title: Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks

abstract:
  Exact algorithms for learning Bayesian networks guarantee to find provably
optimal networks. However, they may fail in difficult learning tasks due to
limited time or memory. In this research we adapt several anytime heuristic
search-based algorithms to learn Bayesian networks. These algorithms find
high-quality solutions quickly, and continually improve the incumbent solution
or prove its optimality before resources are exhausted. Empirical results show
that the anytime window A* algorithm usually finds higher-quality, often
optimal, networks more quickly than other approaches. The results also show
that, surprisingly, while generating networks with few parents per variable are
structurally simpler, they are harder to learn than complex generating networks
with more parents per variable.


title: On the Complexity of Strong and Epistemic Credal Networks

abstract:
  Credal networks are graph-based statistical models whose parameters take
values in a set, instead of being sharply specified as in traditional
statistical models (e.g., Bayesian networks). The computational complexity of
inferences on such models depends on the irrelevance/independence concept
adopted. In this paper, we study inferential complexity under the concepts of
epistemic irrelevance and strong independence. We show that inferences under
strong independence are NP-hard even in trees with ternary variables. We prove
that under epistemic irrelevance the polynomial time complexity of inferences
in credal trees is not likely to extend to more general models (e.g. singly
connected networks). These results clearly distinguish networks that admit
efficient inferences and those where inferences are most likely hard, and
settle several open questions regarding computational complexity.


title:
Learning Periodic Human Behaviour Models from Sparse Data for
  Crowdsourcing Aid Delivery in Developing Countries

abstract:
  In many developing countries, half the population lives in rural locations,
where access to essentials such as school materials, mosquito nets, and medical
supplies is restricted. We propose an alternative method of distribution (to
standard road delivery) in which the existing mobility habits of a local
population are leveraged to deliver aid, which raises two technical challenges
in the areas optimisation and learning. For optimisation, a standard Markov
decision process applied to this problem is intractable, so we provide an exact
formulation that takes advantage of the periodicities in human location
behaviour. To learn such behaviour models from sparse data (i.e., cell tower
observations), we develop a Bayesian model of human mobility. Using real cell
tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we
find that our model outperforms the state of the art approaches in mobility
prediction by at least 25% (in held-out data likelihood). Furthermore, when
incorporating mobility prediction with our MDP approach, we find a 81.3%
reduction in total delivery time versus routine planning that minimises just
the number of participants in the solution path.


title: Learning Max-Margin Tree Predictors

abstract:
  Structured prediction is a powerful framework for coping with joint
prediction of interacting outputs. A central difficulty in using this framework
is that often the correct label dependence structure is unknown. At the same
time, we would like to avoid an overly complex structure that will lead to
intractable prediction. In this work we address the challenge of learning tree
structured predictive models that achieve high accuracy while at the same time
facilitate efficient (linear time) inference. We start by proving that this
task is in general NP-hard, and then suggest an approximate alternative.
Briefly, our CRANK approach relies on a novel Circuit-RANK regularizer that
penalizes non-tree structures and that can be optimized using a CCCP procedure.
We demonstrate the effectiveness of our approach on several domains and show
that, despite the relative simplicity of the structure, prediction accuracy is
competitive with a fully connected model that is computationally costly at
prediction time.


title: Tighter Linear Program Relaxations for High Order Graphical Models

abstract:
  Graphical models with High Order Potentials (HOPs) have received considerable
interest in recent years. While there are a variety of approaches to inference
in these models, nearly all of them amount to solving a linear program (LP)
relaxation with unary consistency constraints between the HOP and the
individual variables. In many cases, the resulting relaxations are loose, and
in these cases the results of inference can be poor. It is thus desirable to
look for more accurate ways of performing inference in these models. In this
work, we study the LP relaxations that result from enforcing additional
consistency constraints between the HOP and the rest of the model. We address
theoretical questions about the strength of the resulting relaxations compared
to the relaxations that arise in standard approaches, and we develop practical
and efficient message passing algorithms for optimizing the LPs. Empirically,
we show that the LPs with additional consistency constraints lead to more
accurate inference on some challenging problems that include a combination of
low order and high order terms.


title: Cyclic Causal Discovery from Continuous Equilibrium Data

abstract:
  We propose a method for learning cyclic causal models from a combination of
observational and interventional equilibrium data. Novel aspects of the
proposed method are its ability to work with continuous data (without assuming
linearity) and to deal with feedback loops. Within the context of biochemical
reactions, we also propose a novel way of modeling interventions that modify
the activity of compounds instead of their abundance. For computational
reasons, we approximate the nonlinear causal mechanisms by (coupled) local
linearizations, one for each experimental condition. We apply the method to
reconstruct a cellular signaling network from the flow cytometry data measured
by Sachs et al. (2005). We show that our method finds evidence in the data for
feedback loops and that it gives a more accurate quantitative description of
the data at comparable model complexity.


title: Structured Convex Optimization under Submodular Constraints

abstract:
  A number of discrete and continuous optimization problems in machine learning
are related to convex minimization problems under submodular constraints. In
this paper, we deal with a submodular function with a directed graph structure,
and we show that a wide range of convex optimization problems under submodular
constraints can be solved much more efficiently than general submodular
optimization methods by a reduction to a maximum flow problem. Furthermore, we
give some applications, including sparse optimization methods, in which the
proposed methods are effective. Additionally, we evaluate the performance of
the proposed method through computational experiments.


title: Treedy: A Heuristic for Counting and Sampling Subsets

abstract:
  Consider a collection of weighted subsets of a ground set N. Given a query
subset Q of N, how fast can one (1) find the weighted sum over all subsets of
Q, and (2) sample a subset of Q proportionally to the weights? We present a
tree-based greedy heuristic, Treedy, that for a given positive tolerance d
answers such counting and sampling queries to within a guaranteed relative
error d and total variation distance d, respectively. Experimental results on
artificial instances and in application to Bayesian structure discovery in
Bayesian networks show that approximations yield dramatic savings in running
time compared to exact computation, and that Treedy typically outperforms a
previously proposed sorting-based heuristic.


title: Stochastic Rank Aggregation

abstract:
  This paper addresses the problem of rank aggregation, which aims to find a
consensus ranking among multiple ranking inputs. Traditional rank aggregation
methods are deterministic, and can be categorized into explicit and implicit
methods depending on whether rank information is explicitly or implicitly
utilized. Surprisingly, experimental results on real data sets show that
explicit rank aggregation methods would not work as well as implicit methods,
although rank information is critical for the task. Our analysis indicates that
the major reason might be the unreliable rank information from incomplete
ranking inputs. To solve this problem, we propose to incorporate uncertainty
into rank aggregation and tackle the problem in both unsupervised and
supervised scenario. We call this novel framework {stochastic rank aggregation}
(St.Agg for short). Specifically, we introduce a prior distribution on ranks,
and transform the ranking functions or objectives in traditional explicit
methods to their expectations over this distribution. Our experiments on
benchmark data sets show that the proposed St.Agg outperforms the baselines in
both unsupervised and supervised scenarios.


title:
Extention of the Time-Dependent Multi-Determinant approach to
  propagators

abstract:
  We extend the recently proposed Time-Dependent Multi-Determinant approach
(ref.[1]) to the description of fermionic propagators. The method hinges on
equations of motions obtained using variational principles of Dirac type. In
particular we focus on traces of imaginary time propagators, i.e. the partition
function. The method is equally applicable with or without projectors to good
quantum numbers.


title: Pay or Play

abstract:
  We introduce the class of pay or play games, which captures scenarios in
which each decision maker is faced with a choice between two actions: one with
a fixed payoff and an- other with a payoff dependent on others' selected
actions. This is, arguably, the simplest setting that models selection among
certain and uncertain outcomes in a multi-agent system. We study the properties
of equilibria in such games from both a game-theoretic perspective and a
computational perspective. Our main positive result establishes the existence
of a semi-strong equilibrium in every such game. We show that although simple,
pay of play games contain a large variety of well-studied environments, e.g.,
vaccination games. We discuss the interesting implications of our results for
these environments.


title: Evaluating computational models of explanation using human judgments

abstract:
  We evaluate four computational models of explanation in Bayesian networks by
comparing model predictions to human judgments. In two experiments, we present
human participants with causal structures for which the models make divergent
predictions and either solicit the best explanation for an observed event
(Experiment 1) or have participants rate provided explanations for an observed
event (Experiment 2). Across two versions of two causal structures and across
both experiments, we find that the Causal Explanation Tree and Most Relevant
Explanation models provide better fits to human data than either Most Probable
Explanation or Explanation Tree models. We identify strengths and shortcomings
of these models and what they can reveal about human explanation. We conclude
by suggesting the value of pursuing computational and psychological
investigations of explanation in parallel.


title:
Approximation of Lorenz-Optimal Solutions in Multiobjective Markov
  Decision Processes

abstract:
  This paper is devoted to fair optimization in Multiobjective Markov Decision
Processes (MOMDPs). A MOMDP is an extension of the MDP model for planning under
uncertainty while trying to optimize several reward functions simultaneously.
This applies to multiagent problems when rewards define individual utility
functions, or in multicriteria problems when rewards refer to different
features. In this setting, we study the determination of policies leading to
Lorenz-non-dominated tradeoffs. Lorenz dominance is a refinement of Pareto
dominance that was introduced in Social Choice for the measurement of
inequalities. In this paper, we introduce methods to efficiently approximate
the sets of Lorenz-non-dominated solutions of infinite-horizon, discounted
MOMDPs. The approximations are polynomial-sized subsets of those solutions.


title:
Solution Methods for Constrained Markov Decision Process with Continuous
  Probability Modulation

abstract:
  We propose solution methods for previously-unsolved constrained MDPs in which
actions can continuously modify the transition probabilities within some
acceptable sets. While many methods have been proposed to solve regular MDPs
with large state sets, there are few practical approaches for solving
constrained MDPs with large action sets. In particular, we show that the
continuous action sets can be replaced by their extreme points when the rewards
are linear in the modulation. We also develop a tractable optimization
formulation for concave reward functions and, surprisingly, also extend it to
non- concave reward functions by using their concave envelopes. We evaluate the
effectiveness of the approach on the problem of managing delinquencies in a
portfolio of loans.


title:
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature
  Models

abstract:
  We propose a probabilistic model to infer supervised latent variables in the
Hamming space from observed data. Our model allows simultaneous inference of
the number of binary latent variables, and their values. The latent variables
preserve neighbourhood structure of the data in a sense that objects in the
same semantic concept have similar latent values, and objects in different
concepts have dissimilar latent values. We formulate the supervised infinite
latent variable problem based on an intuitive principle of pulling objects
together if they are of the same type, and pushing them apart if they are not.
We then combine this principle with a flexible Indian Buffet Process prior on
the latent variables. We show that the inferred supervised latent variables can
be directly used to perform a nearest neighbour search for the purpose of
retrieval. We introduce a new application of dynamically extending hash codes,
and show how to effectively couple the structure of the hash codes with
continuously growing structure of the neighbourhood preserving infinite latent
feature space.


title:
Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition
  Function

abstract:
  A recent result has demonstrated that the Bethe partition function always
lower bounds the true partition function of binary, log-supermodular graphical
models. We demonstrate that these results can be extended to other interesting
classes of graphical models that are not necessarily binary or
log-supermodular: the ferromagnetic Potts model with a uniform external field
and its generalizations and special classes of weighted graph homomorphism
problems.


title:
Identifying Finite Mixtures of Nonparametric Product Distributions and
  Causal Inference of Confounders

abstract:
  We propose a kernel method to identify finite mixtures of nonparametric
product distributions. It is based on a Hilbert space embedding of the joint
distribution. The rank of the constructed tensor is equal to the number of
mixture components. We present an algorithm to recover the components by
partitioning the data points into clusters such that the variables are jointly
conditionally independent given the cluster. This method can be used to
identify finite confounders.


title: A semi-empirical stability criterion for real planetary systems

abstract:
  We test a crossing orbit stability criterion for eccentric planetary systems,
based on Wisdom's criterion of first order mean motion resonance overlap
(Wisdom, 1980).
  We show that this criterion fits the stability regions in real exoplanet
systems quite well. In addition, we show that elliptical orbits can remain
stable even for regions where the apocenter distance of the inner orbit is
larger than the pericenter distance of the outer orbit, as long as the initial
orbits are aligned.
  The analytical expressions provided here can be used to put rapid constraints
on the stability zones of multi-planetary systems. As a byproduct of this
research, we further show that the amplitude variations of the eccentricity can
be used as a fast-computing stability indicator.


title:
Determinantal Clustering Processes - A Nonparametric Bayesian Approach
  to Kernel Based Semi-Supervised Clustering

abstract:
  Semi-supervised clustering is the task of clustering data points into
clusters where only a fraction of the points are labelled. The true number of
clusters in the data is often unknown and most models require this parameter as
an input. Dirichlet process mixture models are appealing as they can infer the
number of clusters from the data. However, these models do not deal with high
dimensional data well and can encounter difficulties in inference. We present a
novel nonparameteric Bayesian kernel based method to cluster data points
without the need to prespecify the number of clusters or to model complicated
densities from which data points are assumed to be generated from. The key
insight is to use determinants of submatrices of a kernel matrix as a measure
of how close together a set of points are. We explore some theoretical
properties of the model and derive a natural Gibbs based algorithm with MCMC
hyperparameter learning. The model is implemented on a variety of synthetic and
real world data sets.


title: Sparse Nested Markov models with Log-linear Parameters

abstract:
  Hidden variables are ubiquitous in practical data analysis, and therefore
modeling marginal densities and doing inference with the resulting models is an
important problem in statistics, machine learning, and causal inference.
Recently, a new type of graphical model, called the nested Markov model, was
developed which captures equality constraints found in marginals of directed
acyclic graph (DAG) models. Some of these constraints, such as the so called
`Verma constraint', strictly generalize conditional independence. To make
modeling and inference with nested Markov models practical, it is necessary to
limit the number of parameters in the model, while still correctly capturing
the constraints in the marginal of a DAG model. Placing such limits is similar
in spirit to sparsity methods for undirected graphical models, and regression
models. In this paper, we give a log-linear parameterization which allows
sparse modeling with nested Markov models. We illustrate the advantages of this
parameterization with a simulation study.


title: Preference Elicitation For General Random Utility Models

abstract:
  This paper discusses {General Random Utility Models (GRUMs)}. These are a
class of parametric models that generate partial ranks over alternatives given
attributes of agents and alternatives. We propose two preference elicitation
scheme for GRUMs developed from principles in Bayesian experimental design, one
for social choice and the other for personalized choice. We couple this with a
general Monte-Carlo-Expectation-Maximization (MC-EM) based algorithm for MAP
inference under GRUMs. We also prove uni-modality of the likelihood functions
for a class of GRUMs. We examine the performance of various criteria by
experimental studies, which show that the proposed elicitation scheme increases
the precision of estimation.


title: Modeling Documents with Deep Boltzmann Machines

abstract:
  We introduce a Deep Boltzmann Machine model suitable for modeling and
extracting latent semantic representations from a large unstructured collection
of documents. We overcome the apparent difficulty of training a DBM with
judicious parameter tying. This parameter tying enables an efficient
pretraining algorithm and a state initialization scheme that aids inference.
The model can be trained just as efficiently as a standard Restricted Boltzmann
Machine. Our experiments show that the model assigns better log probability to
unseen data than the Replicated Softmax model. Features extracted from our
model outperform LDA, Replicated Softmax, and DocNADE models on document
retrieval and document classification tasks.


title:
A self-similar cascade of instabilities in the surface quasigeostrophic
  system

abstract:
  We provide numerical evidence for the existence of a cascade of filament
instabilities in the surface quasigeostrophic system for atmospheric and
oceanic motions near a horizontal boundary. The cascade involves geometrically
shrinking spatial and temporal scales and implies the singular collapse of the
filament width to zero in a finite time. The numerical method is both spatially
and temporally adaptive, permitting the accurate simulation of the evolution
over an unprecedented range of spatial scales spanning over ten orders of
magnitude. It provides the first convincing demonstration of the cascade, in
which the large separation of scales between subsequent instabilities has made
previous numerical simulation difficult.


title: Speedy Model Selection (SMS) for Copula Models

abstract:
  We tackle the challenge of efficiently learning the structure of expressive
multivariate real-valued densities of copula graphical models. We start by
theoretically substantiating the conjecture that for many copula families the
magnitude of Spearman's rank correlation coefficient is monotone in the
expected contribution of an edge in network, namely the negative copula
entropy. We then build on this theory and suggest a novel Bayesian approach
that makes use of a prior over values of Spearman's rho for learning
copula-based models that involve a mix of copula families. We demonstrate the
generalization effectiveness of our highly efficient approach on sizable and
varied real-life datasets.


title: Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs

abstract:
  We seek to learn an effective policy for a Markov Decision Process (MDP) with
continuous states via Q-Learning. Given a set of basis functions over state
action pairs we search for a corresponding set of linear weights that minimizes
the mean Bellman residual. Our algorithm uses a Kalman filter model to estimate
those weights and we have developed a simpler approximate Kalman filter model
that outperforms the current state of the art projected TD-Learning methods on
several standard benchmark problems.


title: Finite-Time Analysis of Kernelised Contextual Bandits

abstract:
  We tackle the problem of online reward maximisation over a large finite set
of actions described by their contexts. We focus on the case when the number of
actions is too big to sample all of them even once. However we assume that we
have access to the similarities between actions' contexts and that the expected
reward is an arbitrary linear function of the contexts' images in the related
reproducing kernel Hilbert space (RKHS). We propose KernelUCB, a kernelised UCB
algorithm, and give a cumulative regret bound through a frequentist analysis.
For contextual bandits, the related algorithm GP-UCB turns out to be a special
case of our algorithm, and our finite-time analysis improves the regret bound
of GP-UCB for the agnostic case, both in the terms of the kernel-dependent
quantity and the RKHS norm of the reward function. Moreover, for the linear
kernel, our regret bound matches the lower bound for contextual linear bandits.


title: Dynamic Blocking and Collapsing for Gibbs Sampling

abstract:
  In this paper, we investigate combining blocking and collapsing -- two widely
used strategies for improving the accuracy of Gibbs sampling -- in the context
of probabilistic graphical models (PGMs). We show that combining them is not
straight-forward because collapsing (or eliminating variables) introduces new
dependencies in the PGM and in computation-limited settings, this may adversely
affect blocking. We therefore propose a principled approach for tackling this
problem. Specifically, we develop two scoring functions, one each for blocking
and collapsing, and formulate the problem of partitioning the variables in the
PGM into blocked and collapsed subsets as simultaneously maximizing both
scoring functions (i.e., a multi-objective optimization problem). We propose a
dynamic, greedy algorithm for approximately solving this intractable
optimization problem. Our dynamic algorithm periodically updates the
partitioning into blocked and collapsed variables by leveraging correlation
statistics gathered from the generated samples and enables rapid mixing by
blocking together and collapsing highly correlated variables. We demonstrate
experimentally the clear benefit of our dynamic approach: as more samples are
drawn, our dynamic approach significantly outperforms static graph-based
approaches by an order of magnitude in terms of accuracy.