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paper_id stringlengths 10 19	venue stringclasses 14 values	focused_review stringlengths 7 8.45k	point stringlengths 60 643
NIPS_2020_1592	NIPS_2020	Major concerns: 1. While it is impressive that this work gets slightly better results than MLE, there are more hyper-parameters to tune, including mixture weight, proposal temperature, nucleus cutoff, importance weight clipping, MLE pretraining (according to appendix). I find it disappointing that so many tricks are needed. If you get rid of pretraining/initialization from T5/BART, would this method work? 2. This work requires MLE pretraining, while prior work "Training Language GANs from Scratch" does not. 3. For evaluation, since the claim of this paper is to reduce exposure bias, training a discriminator on generations from the learned model is needed to confirm if it is the case, in a way similar to Figure 1. Note that it is different from Figure 4, since during training the discriminator is co-adapting with the generator, and it might get stuck at a local optimum. 4. This work is claiming that it is the first time that language GANs outperform MLE, while prior works like seqGAN or scratchGAN all claim to be better than MLE. Is this argument based on the tradeoff between BLEU and self-BLEU from "language GANs falling short"? If so, Figure 2 is not making a fair comparison since this work uses T5/BART which is trained on external data, while previous works do not. What if you only use in-domain data? Would this still outperform MLE? Minor concerns: 5. This work only uses answer generation and summarization to evaluate the proposed method. While these are indeed conditional generation tasks, they are close to "open domain" generation rather than "close domain" generation such as machine translation. I think this work would be more convincing if it is also evaluated in machine translation which exhibits much lower uncertainties per word. 6. The discriminator accuracy of ~70% looks low to me, compared to "Real or Fake? Learning to Discriminate Machine from Human Generated Text" which achieves almost 90% accuracy. I wonder if the discriminator was not initialized with a pretrained LM, or is that because the discriminator used is too small? ===post-rebuttal=== The added scratch GAN+pretraining (and coldGAN-pretraining) experiments are fairer, but scratch GAN does not need MLE pretraining while this work does, and we know that MLE pretraining makes a big difference, so I am still not very convinced. My main concern is the existence of so many hyper-parameters/tricks: mixture weight, proposal temperature, nucleus cutoff, importance weight clipping, and MLE pretraining. I think some sensitivity analysis similar to scratch GAN's would be very helpful. In addition, rebuttal Figure 2 is weird: when generating only one word, why would cold GAN already outperform MLE by 10%? To me, this seems to imply that improvement might be due to hyper-parameter tuning.	3. For evaluation, since the claim of this paper is to reduce exposure bias, training a discriminator on generations from the learned model is needed to confirm if it is the case, in a way similar to Figure 1. Note that it is different from Figure 4, since during training the discriminator is co-adapting with the generator, and it might get stuck at a local optimum.
NIPS_2017_631	NIPS_2017	1. The main contribution of the paper is CBN. But the experimental results in the paper are not advancing the state-of-art in VQA (on the VQA dataset which has been out for a while and a lot of advancement has been made on this dataset), perhaps because the VQA model used in the paper on top of which CBN is applied is not the best one out there. But in order to claim that CBN should help even the more powerful VQA models, I would like the authors to conduct experiments on more than one VQA model â favorably the ones which are closer to state-of-art (and whose codes are publicly available) such as MCB (Fukui et al., EMNLP16), HieCoAtt (Lu et al., NIPS16). It could be the case that these more powerful VQA models are already so powerful that the proposed early modulating does not help. So, it is good to know if the proposed conditional batch norm can advance the state-of-art in VQA or not. 2. L170: it would be good to know how much of performance difference this (using different image sizes and different variations of ResNets) can lead to? 3. In table 1, the results on the VQA dataset are reported on the test-dev split. However, as mentioned in the guidelines from the VQA dataset authors (http://www.visualqa.org/vqa_v1_challenge.html), numbers should be reported on test-standard split because one can overfit to test-dev split by uploading multiple entries. 4. Table 2, applying Conditional Batch Norm to layer 2 in addition to layers 3 and 4 deteriorates performance for GuessWhat?! compared to when CBN is applied to layers 4 and 3 only. Could authors please throw some light on this? Why do they think this might be happening? 5. Figure 4 visualization: the visualization in figure (a) is from ResNet which is not finetuned at all. So, it is not very surprising to see that there are not clear clusters for answer types. However, the visualization in figure (b) is using ResNet whose batch norm parameters have been finetuned with question information. So, I think a more meaningful comparison of figure (b) would be with the visualization from Ft BN ResNet in figure (a). 6. The first two bullets about contributions (at the end of the intro) can be combined together. 7. Other errors/typos: a. L14 and 15: repetition of word âimagineâ b. L42: missing reference c. L56: impact -> impacts Post-rebuttal comments: The new results of applying CBN on the MRN model are interesting and convincing that CBN helps fairly developed VQA models as well (the results have not been reported on state-of-art VQA model). So, I would like to recommend acceptance of the paper. However I still have few comments -- 1. It seems that there is still some confusion about test-standard and test-dev splits of the VQA dataset. In the rebuttal, the authors report the performance of the MCB model to be 62.5% on test-standard split. However, 62.5% seems to be the performance of the MCB model on the test-dev split as per table 1 in the MCB paper (https://arxiv.org/pdf/1606.01847.pdf). 2. The reproduced performance reported on MRN model seems close to that reported in the MRN paper when the model is trained using VQA train + val data. I would like the authors to clarify in the final version if they used train + val or just train to train the MRN and MRN + CBN models. And if train + val is being used, the performance can't be compared with 62.5% of MCB because that is when MCB is trained on train only. When MCB is trained on train + val, the performance is around 64% (table 4 in MCB paper). 3. The citation for the MRN model (in the rebuttal) is incorrect. It should be -- @inproceedings{kim2016multimodal, title={Multimodal residual learning for visual qa}, author={Kim, Jin-Hwa and Lee, Sang-Woo and Kwak, Donghyun and Heo, Min-Oh and Kim, Jeonghee and Ha, Jung-Woo and Zhang, Byoung-Tak}, booktitle={Advances in Neural Information Processing Systems}, pages={361--369}, year={2016} } 4. As AR2 and AR3, I would be interested in seeing if the findings from ResNet carry over to other CNN architectures such as VGGNet as well.	2.L170: it would be good to know how much of performance difference this (using different image sizes and different variations of ResNets) can lead to?
NIPS_2022_528	NIPS_2022	weakness 1 The Algorithm should be presented and described in detail. 2 The background of Sharpness-Aware Minimization (SAM) shoud be described in detail. 1 The Algorithm should be presented and described in detail, which is helpful for understanding the proposed method. 2 The background of Sharpness-Aware Minimization (SAM) shoud be described in detail.	1 The Algorithm should be presented and described in detail, which is helpful for understanding the proposed method.
NIPS_2016_321	NIPS_2016	#ERROR!	- Since the paper mentions the possibility to use Chebyshev polynomials to achieve a speed-up, it would have been interesting to see a runtime comparison at test time.
NIPS_2020_11	NIPS_2020	1. The proposed method seems only works for digit or text images, such as MNIST and SVHN. Can it be used on natural images, such as CIFAR10, which has wider applications in the real world then digit/text. 2. Are the results obtained on Synbols dataset generalizable to large-scale datasets? For example, if you find algorithm A is better than B on Synbols dataset, will the conclusion hold on large images (e.g., ImageNet scale) in real-world applications? This need to be discussed in the paper.	1. The proposed method seems only works for digit or text images, such as MNIST and SVHN. Can it be used on natural images, such as CIFAR10, which has wider applications in the real world then digit/text.
tbRPPWDy76	EMNLP_2023	There might be not enough theoretical discussion and in-depth analyses which help readers understand the prompt design. More motivations and insights are needed. The engineering part might still need refinement. * Considering that this work is all about evaluation, there might be a lack of experiments currently. It might be beneficial to conduct more evaluation experiments categorized by language types and dialog content. * Although the style design is clean, the prompts are not well-organized (Table 6, 7). All sentences squeeze together. * The Chinese translation of the proposed prompt (Table 7) is bad.	* Although the style design is clean, the prompts are not well-organized (Table 6, 7). All sentences squeeze together.
ICLR_2023_2622	ICLR_2023	Weakness: 1. The figures are not clear. For example, in figure 2, it’s confused for the relation of 3 sub-figures. Some modules are not labeled in figure, such as CMAF, L_BT, VoLTA. 2. The experiments results are not significant. 3. Three steps for training are shown in VoLTA, a) switching off CMAF, b) switching on CMAF, c) keep CMAF and random sampling for training. The ablation study on these parts should be conducted. 4. The key point of this paper is GOT, but no ablation on this part. The authors are encouraged to verify which parts works.	1. The figures are not clear. For example, in figure 2, it’s confused for the relation of 3 sub-figures. Some modules are not labeled in figure, such as CMAF, L_BT, VoLTA.
ARR_2022_314_review	ARR_2022	1. Although the work is important and detailed, from the novelty perspective, it is an extension of norm-based and rollout aggregation methods to another set of residual connections and norm layer in the encoder block. Not a strong weakness, as the work makes a detailed qualitative and quantitative analysis, roles of each component, which is a novelty in its own right. 2. The impact of the work would be more strengthened with the proposed approach's (local and global) applicability to tasks other than classification like question answering, textual similarity, etc. ( Like in the previous work, Kobayashi et al. (2020)) 1. For equations 12 and 13, authors assume equal contribution from the residual connection and multi-head attention. However, in previous work by Kobayashi et al. (2021), it is observed and revealed that residual connections have a huge impact compared to mixing (attention). This assumption seems to be the opposite of the observations made previously. What exactly is the reason for that, for simplicity (like assumptions made by Abnar and Zuidema (2020))? 2. At the beginning of the paper, including the abstract and list of contributions, the claim about the components involved is slightly inconsistent with the rest. For instance, the 8th line in abstract is "incorporates all components", line 73 also says the "whole encoder", but on further reading, FFN (Feed forward layers) is omitted from the framework. This needs to be framed (rephrased) better in the beginning to provide a clearer picture. 3. While FFNs are omitted because a linear decomposition cannot be obtained (as mentioned in the paper), is there existing work that offers a way around (an approximation, for instance) to compute the contribution? If not, maybe a line or two should be added that there exists no solution for this, and it is an open (hard) problem. It improves the readability and gives a clearer overall picture to the reader. 4. Will the code be made publicly available with an inference script? It's better to state it in the submission, as it helps in making an accurate judgement that the code will be useful for further research.	3. While FFNs are omitted because a linear decomposition cannot be obtained (as mentioned in the paper), is there existing work that offers a way around (an approximation, for instance) to compute the contribution? If not, maybe a line or two should be added that there exists no solution for this, and it is an open (hard) problem. It improves the readability and gives a clearer overall picture to the reader.
KE5QunlXcr	EMNLP_2023	- The PLMs used (BERT) are by current standards, quite old, and quite small. As work in scaling PLMs up to sizes orders of magnitude greater, performance on syntactic tasks has shown to improve naturally (along with many other useful emergent forms of knowledge). Some comparison to larger models / application of this method to such models, is necessary to ensure that the method has any practical purpose. - There are also no baselines from existing work. There are other forms of fine-tuning, such as adapters, which seek to add additional knowledge to PLMs with less chance of catastrophic forgetting. The authors even cite one of these papers. This is also a confusing oversight, because the many appropriate inline citations which contrast various decisions in this work to decisions in existing work demonstrate a great familiarity with the literation, so lacking any comparison to any existing methods is an odd oversight. - Some questionable design choices. Perplexity is used as a measure of the model retaining semantic information after fine-tuning, and while that does relate to the original task, there are also aspects of domain drift which are possible and separate from catastrophic forgetting. How are such factors controlled? - Related, there is questionable motivation. Often when talking about catastrophic forgetting, both the original training and the new task are both relevant. This is clear in the context of robotics, where learning a new behavior should not result in hindering the robot from performing existing behaviors. Is this true in this case? PPL is almost always not a valuable end goal, and the entire PLM/LLM paradigm is built around this notion of pre-training in whatever way leads to learning useful linguistic representations, before fine-tuning, aligning, or few-shotting the model towards the task the user actually cares about. If users never care about both tasks in approximately equal measure, than what good is retaining the original model weights which were not pertinent to the target task? - There's arguably too much going on here. The focus of the paper aims to be about catastrophic forgetting, but secondary to that, is also the problem of matching the right syntactic fine-tuning task to the right problem. This is not entirely known a priori, so all possible pairings are explored, but realistically a good guess can be made (as it would likely be if pursued in a more practical setting). For instance, it is no surprise that the phrase syntax task helps with key phrase identification. The disadvantage of the exhaustive approach is that it has both distracted from the main takeaway points while cutting into the space available for supporting the main hypothesis. - No inclusion of baselines from existing work / SOTA on performance tables - Key extraction F1 results are better than standard optimizers, but negligibly so. - No discussion of GC vs EWC. When a priori would you choose which method? If the paper included only one such method, traditional optimizers would be the best choice in most situations.	- Some questionable design choices. Perplexity is used as a measure of the model retaining semantic information after fine-tuning, and while that does relate to the original task, there are also aspects of domain drift which are possible and separate from catastrophic forgetting. How are such factors controlled?
ICLR_2021_2208	ICLR_2021	+ Nice idea Consistent improvements over cross entropy for hierarchical class structures Improvements w.r.t other competitors (though not consistent) Good ablation study The improvements are small The novelty is not very significant More comments: Figure 1: - It is not clear what distortion is at this stage - It is not clear what perturbed MNist is, and respectively: why is the error of a 3-layer CNN so high (12-16% error are reported)? CNNs with 2-3 layers can solve MNist with accuracy higher than 99.5%? - This figure cannot be presented on page 2 without proper definitions. It should be either presented on page 5, where the experiment is defined, or better explained Page 4: It is said that s can be computed efficiently and this is shown in the appendix, but the version I have do not have an appendix Page 6: the XE+EMD method is not present in a comprehensible manner. 1) p_k symbols are used without definition (tough I think I these are the network predictions p(\hat{y}=k I) 2) the relation of the formula presented to the known EMD is not clear. The latter is a problem solved as linear programming or similar, and not a closed form formula 3) it is not clear what the role of \mu is and why can be set to 3 irrespective of the scale of metric D page 7: The experiments show small, but consistent improvements of the suggested method over standard cross entropy, and improvements versus most competitors in most cases I have read the reviews of others and the author's response. My main impression of the work remains as it was: that it is nice idea with small but significant empirical success. However, my acquaintance with the previous literature in this subject is partial compared to the acquaintance of other reviewers, so It may well be possible that they are in a better position than me to see the incremental nature of the proposed work. I therefore reduce the rating a bit, to become closer to the consensus.	1) p_k symbols are used without definition (tough I think I these are the network predictions p(\hat{y}=k I) 2) the relation of the formula presented to the known EMD is not clear. The latter is a problem solved as linear programming or similar, and not a closed form formula
NIPS_2021_953	NIPS_2021	Although the paper gives detailed theoretical proof, the experiments are somewhat weak. I still have some concerns: 1）The most related works SwaV and Barlow Twins outperform the proposed method in some experimental results, as shown in Table 1,2,5. What are the main advantages of this method compared with SwaV and Barlow Twins? 2) HSIC(Z, Y) can be seen as a distance metric in the kernel space, where the cluster structure is defined by the identity. Although this paper maps identity labels into the kernel space, the information of one-hot label is somewhat limited compared with views embeddings in Barlow Twins. 3)Since the cluster structure is defined by the identity. How does the number of images impact the model performance? Do more training images make the performance worse or better ? BYOL in the abstract should be explained for its first appearance.	3)Since the cluster structure is defined by the identity. How does the number of images impact the model performance? Do more training images make the performance worse or better ? BYOL in the abstract should be explained for its first appearance.
NIPS_2020_1821	NIPS_2020	- To my understanding, the experimental section only compares results generated for this paper. This is good because it keeps apples-to-apples comparisons, however it is suspicious since the task is not novel. Some comparison with results from other works (or a justification of why this is not possible/suitable) would be welcome. For example [2, table 3] seems to have directly comparable results, yet these are nowhere mentioned in this paper. - Albeit the observed effects are strong, it remains unclear “why does the method work?” in particular regarding the L_pixel component. Providing stronger arguments or intuitions of why these particular losses are “bound to help” would be welcome.	- Albeit the observed effects are strong, it remains unclear “why does the method work?” in particular regarding the L_pixel component. Providing stronger arguments or intuitions of why these particular losses are “bound to help” would be welcome.
NJUzUq2OIi	ICLR_2025	I found the proposed idea, experiments, and analyses conducted by the authors to be valuable, especially in terms of their potential impact on low-resource scenarios. However, for the paper to fully meet the ICLR standards, there are still areas that need additional work and detail. Below, I outline several key points for improvement. I would be pleased to substantially raise my scores if the authors address these suggestions and enhance the paper accordingly. General Feedback - I noticed that the title of the paper does not match the one listed on OpenReview. - The main text should indicate when additional detailed discussions are deferred to the Appendix for better reader guidance. Introduction - The Introduction lacks foundational references to support key claims. Both the second and third paragraphs would benefit from citations to strengthen the arguments. For instance, the statement: "This method eliminates the need for document chunking, a common limitation in current retrieval systems that often results in loss of context and reduced accuracy" needs a supporting citation to substantiate this point. - The sentence: "Second, to be competitive with embedding approaches, a retrieval language model needs to be small" requires further justification. The authors should include in the paper a complexity analysis comparison discussing time and GPU memory consumption to support this assertion. Related Work - The sentence "Large Language Models are found to be inefficient processing long-context documents" should be rewritten for clarity, for example: "Large Language Models are inefficient when processing long-context documents." - The statements "Transformer models suffer from quadratic computation during training and linear computation during inference" and "However, transformer-based models are infeasible to process extremely long documents due to their linear inference time" are incorrect. Transformers, as presented in "Attention is All You Need," scale quadratically in both training and inference. - The statement regarding State Space Models (SSMs) having "linear scaling during training and constant scaling during inference" is inaccurate. SSMs have linear complexity for both training and inference. The term "constant scaling" implies no dependence on sequence length, which is incorrect. - The Related Work section is lacking details. The paragraph on long-context language models should provide a more comprehensive overview of existing methods and their limitations, positioning SSMs appropriately. This includes discussing sparse-attention mechanisms [1, 2], segmentation-based approaches [3, 4, 5], memory-enhanced segmentation strategies [6], and recursive methods [7] for handling very long documents. - Similarly, the paragraph on Retrieval-Augmented Generation should specify how prior works addressed different long document tasks. Examples include successful applications of RAG in long-document summarization [8, 9] and query-focused multi-document summarization [10, 11], which are closely aligned with the present work. Figures - Figures 1 and 2 are clear but need aesthetic improvements to meet the conference's standard presentation quality. Model Architecture - The description "a subset of tokens are specially designated, and the classification head is applied to these tokens. In the current work, the classification head is applied to the last token of each sentence, giving sentence-level resolution" is ambiguous. Clarify whether new tokens are added to the sequence or if existing tokens (e.g., periods) are used to represent sentence ends. Synthetic Data Generation - The "lost in the middle" problem when processing long documents [12] is not explicitly discussed. Have the authors considered the position of chunks during synthetic data generation? Ablation studies varying the position and distance between linked chunks would provide valuable insights into Mamba’s effectiveness in addressing this issue. - More details are needed regarding the data decontamination pipeline, chunk size, and the relative computational cost of the link-based method versus other strategies. - The authors claim that synthetic data generation is computationally expensive but provide no supporting quantitative evidence. Information such as time estimates and GPU demand would strengthen this argument and assess feasibility. - There is no detailed evaluation of the synthetic data’s quality. An analysis of correctness and answer factuality would help validate the impact on retrieval performance beyond benchmark metrics. Training - This section is too brief. Consider merging it with Section 3, "Model Architecture," for a more cohesive presentation. - What was the training time for the 130M model? Experimental Method - Fix minor formatting issues, such as adding a space after the comma in ",LVeval." - Specify in Table 1 which datasets use free-form versus multiple-choice answers, including the number of answers and average answer lengths. - Consider experimenting with GPT-4 as a retriever. - Expand on "The accuracy of freeform answers is judged using GPT-4." - Elaborate on the validation of the scoring pipeline, particularly regarding "0.942 macro F1." Clarify the data and method used for validation. - Justify the selection of "50 sentences" for Mamba retrievers and explain chunk creation methods for embedding models. Did the chunks consist of 300 fixed-length segments, or was semantic chunking employed [3, 5]? Sentence-level embedding-based retrieval could be explored to align better with the Mamba setting. - The assertion that "embedding models were allowed to retrieve more information than Mamba" implies an unfair comparison, but more context can sometimes degrade performance [12]. - Clarify the use of the sliding window approach for documents longer than 128k tokens, especially given the claim that Mamba could process up to 256K tokens directly. Results - Remove redundancy in Section 7.1.2, such as restating the synthetic data generation strategies. - Expand the ablation studies to cover different input sequence lengths during training and varying the number of retrieved sentences to explore robustness to configuration changes. - Highlight that using fewer training examples (500K vs. 1M) achieved comparable accuracy (i.e., 59.4 vs. 60.0, respectively). - Why not train both the 130M and 1.3B models on a dataset size of 500K examples, but compare using 1M and 400K examples, respectively? Limitations - The high cost of generating synthetic training data is mentioned but lacks quantification. How computationally expensive is it in terms of time or resources? Appendix - Note that all figures in Appendices B and C are the same, suggesting an error that needs correcting. Missing References [1] Longformer: The Long-Document Transformer. arXiv 2020. [2] LongT5: Efficient Text-To-Text Transformer for Long Sequences. NAACL 2022. [3] Semantic Self-Segmentation for Abstractive Summarization of Long Documents in Low-Resource Regimes. AAAI 2022. [4] Summ^n: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents. ACL 2022. [5] Align-Then-Abstract Representation Learning for Low-Resource Summarization. Neurocomputing 2023. [6] Efficient Memory-Enhanced Transformer for Long-Document Summarization in Low-Resource Regimes. Sensors 2023. [7] Recursively Summarizing Books with Human Feedback. arXiv 2021. [8] DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization. ACL 2022. [9] Towards a Robust Retrieval-Based Summarization System. arXiv 2024. [10] Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature. ACL 2022. [11] Retrieve-and-Rank End-to-End Summarization of Biomedical Studies. SISAP 2023. [12] Lost in the Middle: How Language Models Use Long Contexts. TACL 2024.	- The Related Work section is lacking details. The paragraph on long-context language models should provide a more comprehensive overview of existing methods and their limitations, positioning SSMs appropriately. This includes discussing sparse-attention mechanisms [1, 2], segmentation-based approaches [3, 4, 5], memory-enhanced segmentation strategies [6], and recursive methods [7] for handling very long documents.
eCXfUq3RDf	EMNLP_2023	1. Very limited reproducibility - Unless the authors release their training code, dialogue dataset, as well as model checkpoints, I find it very challenging to reproduce any of the claims in this paper. I encourage the authors to attach their code and datasets via anonymous repositories in the paper submission so that reviewers may verify the claims and try out the model for themselves. 2. Very high model complexity - The proposed paper employs a mathematically and computationally complex approach as compared to the textual input method. Does the proposed method outperform sending textual inputs to a large foundation model such as LLAMA or ChatGPT? The training complexity seems too high for any practical deployment of this model. 3. Dependent on the training data - I'm unsure if 44k dialogues is sufficient to capture a wide range of user traits and personalities across different content topics. LLMs are typically trained on trillions of tokens, I do not see how 44k dialogues can capture the combinations of personalities and topics. In theory, this dataset also needs to be massive to cover varied domains. 4. The paper is hard to read and often unintuitive. The mathematical complexity must be simplified and replaced with more intuitive design and modeling choice explanations so that readers may grasp core ideas faster.	3. Dependent on the training data - I'm unsure if 44k dialogues is sufficient to capture a wide range of user traits and personalities across different content topics. LLMs are typically trained on trillions of tokens, I do not see how 44k dialogues can capture the combinations of personalities and topics. In theory, this dataset also needs to be massive to cover varied domains.
Uj2Wjv0pMY	ICLR_2024	• Compared to Assembly 101 (error detection), the paper seems like an inferior / less complicated dataset. Claims like higher ratio of error to normal videos needs to be validated. • Compared to datasets, the dataset prides itself on adding different modalities especially depth channel (RGB-D). The paper fails to validate the necessities of such modality. One crucial different between assembly dataset is use of depth values. What role does it play in training baseline models? Does it boost the model’s performance if these weren’t present. In current deep learning area, depth channels should be reasonably be producible via the help of existing models. • I’m not convinced that the binary classification is a justifiable baseline metrics. While I agree with the TAL task is really important here and a good problem to solve, I’m not sure how coarse grained binary classification can assess models understanding of fine-grained error like technique error. • Timing Error (Duration of an activity) and Temperature based error, does these really need ML based solutions? In sensitive tasks, simple sensor reading can indicate error. I’m not sure testing computer vision models on such tasks is justifiable. These require more heuristics-based methods, working with if-else statement. • Procedure Learning: its very vaguely defined, mostly left unexplained and seems like an after thought. I recommend authors devote passage to methods “M1 (Dwibedi et al., 2019)” and “M2 (Bansal, Siddhant et al., 2022)”. In Table 5, value of lambda? Is not mentioned. • The authors are dealing with a degree of subjectivity in terms of severity of errors. It would have been greatly beneficial, if the errors could be finely measured. For example if the person uses a tablespoon instead of teaspoon, is still an error? Some errors are more grave than others, is there a weighted scores? Is there a way to measure level of deviation for each type of error or time stamp of occurrence of error. Is one recipe more difficult than the other recipe.	• I’m not convinced that the binary classification is a justifiable baseline metrics. While I agree with the TAL task is really important here and a good problem to solve, I’m not sure how coarse grained binary classification can assess models understanding of fine-grained error like technique error.
NIPS_2019_653	NIPS_2019	of the method. Clarity: The paper has been written in a manner that is straightforward to read and follow. Significance: There are two factors which dent the significance of this work. 1. The work uses only binary features. Real world data is usually a mix of binary, real and categorical features. It is not clear if the method is applicable to real and categorical features too. 2. The method does not seem to be scalable, unless a distributed version of it is developed. It's not reasonable to expect a single instance can hold all the training data that the real world datasets ususally contain.	1. The work uses only binary features. Real world data is usually a mix of binary, real and categorical features. It is not clear if the method is applicable to real and categorical features too.
NIPS_2018_857	NIPS_2018	Weakness: - Long range contexts may be helpful for object detection as shown in [a, b]. For example, the sofa in Figure 1 may help detect the monitor. But in the SNIPER, images are cropped into chips, which makes the detector cannot benefit from long range contexts. Is there any idea to address this? - The writing should be improved. Some points in the paper is unclear to me. 1. In line 121, authors said partially overlapped ground-truth instances are cropped. But is there any threshold for the partial overlap? In the lower left figure of the Figure 1 right side, there is a sofa whose bounding-box is partially overlapped with the chip, but not shown in a red rectangle. 2. In line 165, authors claimed that a large object which may generate a valid small proposal after being cropped. This is a follow-up question of the previous one. In the upper left figure of the Figure 1 right side, I would imagine the corner of the sofa would make some very small proposals to be valid and labelled as sofa. Does that distract the training process since there may be too little information to classify the little proposal to sofa? 3. Are the negative chips fixed after being generated from the lightweight RPN? Or they will be updated while the RPN is trained in the later stage? Would this (alternating between generating negative chips and train the network) help the performance? 4. What are the r^i_{min}'s, r^i_{max}'s and n in line 112? 5. In the last line of table3, the AP50 is claimed to be 48.5. Is it a typo? [a] Wang et al. Non-local neural networks. In CVPR 2018. [b] Hu et al. Relation Networks for Object Detection. In CVPR 2018. ----- Authors' response addressed most of my questions. After reading the response, I'd like to remain my overall score. I think the proposed method is useful in object detection by enabling BN and improving the speed, and I vote for acceptance. The writing issues should be fixed in the later versions.	- The writing should be improved. Some points in the paper is unclear to me.
9Ax0pyaLgh	EMNLP_2023	1. Authors are suggested to use other metrics to evaluate the Results (e.g. BERTScore). 2. Often it is not sufficient to show automatic evaluation results. The author does not show any human evaluation results and does not even perform a case study and proper error analysis. This does not reflect well on the qualitative aspects of the proposed model. 3. It is difficult to understand the methodology without Figure 1. Parts of section 2 should be written in alignment with Figure 1, and the authors are expected to follow a step-by-step description of the proposed method. (See questions to authors)	1. Authors are suggested to use other metrics to evaluate the Results (e.g. BERTScore).
6iM2asNCjK	ICLR_2024	1. My primary concern is with the limited scope of the paper. The paper primarily considers only evaluating sentence embeddings from LLMs, which while important, is a small part of the overall evaluation landscape of LLMs. Consequently, the title "Robustness-Accuracy characterization of Large Language Models using synthetic datasets" is somewhat misleading. Furthermore, the generation methodology for the synthetic tasks using SentiWordNet for polarity detection does seem somewhat restrictive. For sentence embedding evaluation, it does seem to be a good methodology, but it is not clear how well it would generalize to any generative tasks (e.g. question answering, summarization, etc.). Whether this metric can be leveraged for other tasks (especially for a different class of tasks) needs to be demonstrated in my opinion. 2. While the proposed methodology of using a ratio of positive / negative to neutral sentiment words is a good way of defining difficulty, it does seem somewhat restrictive given the contextual nature of languages. Interesting linguistic phenomena such as sarcasm, irony, etc. are not captured by the proposed methodology, which arguably form for a large part of the difficulty in language understanding especially for such large LLMs. While the authors briefly touch upon the issue of negation, negation in natural language is not limited to structured rules, and any methodology testing the robustness of LLMs should provide a way of capturing this, given that LLMs are generally have a poor understanding of negations ([1]). 3. The baseline metrics are still computed on the synthetic dataset. For a generative LLM model training for example, this potentially results in bad sentence embeddings, which subsequently may result in bad task performance. This is especially problematic when done for a single dataset (as is the case for all the baseline metrics). In contrast, the proposed SynTextBench benefits from aggregating across different difficulty levels, and is somewhat more robust to this issue compared to the baseline metrics. A better way for considering the baselines might be to treat them in the same way as SynTextBench is treated (aggregated across different difficulty levels, thresholded for some value of the metric, and then computing the area under the curve). 4. Additionally, there has been a large amount of work on LLM evaluation [2]. While some of the metrics there do not satisfy the proposed desiderata, it would still be good to see how SynTextBench metric compares to the other metrics proposed in the literature. Concretely, from the paper, it is hard to understand under what conditions should one use SynTextBench over other metrics (eg: say MMLU / Big Bench for language generation).	4. Additionally, there has been a large amount of work on LLM evaluation [2]. While some of the metrics there do not satisfy the proposed desiderata, it would still be good to see how SynTextBench metric compares to the other metrics proposed in the literature. Concretely, from the paper, it is hard to understand under what conditions should one use SynTextBench over other metrics (eg: say MMLU / Big Bench for language generation).
ICLR_2022_2425	ICLR_2022	1)Less Novelty: The algorithm for construction of coresets itself is not novel. Existing coreset frameworks for classical k-means and (k,z) clusterings are extended to the kernelized setting. 2)Clarity: Since the coreset construction algorithm is built up on previous works, a reader without the background in literature on coresets would find it hard to understand why the particular sampling probabilities are chosen and why they give particular guarantees. It would be useful rewrite the algorithm preview and to give at least a bit of intuition on how the importance sampling scores are chosen and how they can give the coreset guarantees Suggestions: In the experiment section, other than uniform sampling, it would be interesting to use some other classical k-means coreset as baselines for comparison. Please highlight the technical challenges and contributions clearly when compared to coresets for classical k-means.	1)Less Novelty: The algorithm for construction of coresets itself is not novel. Existing coreset frameworks for classical k-means and (k,z) clusterings are extended to the kernelized setting.
NIPS_2019_203	NIPS_2019	* Technical innovation is fairly limited. The bLVNet is a straightforward extension of bLNet (an image model) to video. The TAM involves the use of 1D temporal convolution and depthwise convolution. Both mechanisms that have been widely leveraged before. On the other hand, the paper does not make bold novelty claims and recognizes the contribution as being more empirical than technical. The TAM shares many similarities with Timeception [Hussein et al., CVPR 19], which was not yet published at the time of this submission and thus does not diminish the value of this work. Nevertheless, given the many analogies between these concurrent approaches, it'd be advisable to discuss their relations in future versions (or the camera-ready version) of the paper. * While the memory/efficiency gains are convincingly demonstrated, they are not substantial enough to be a game-changer in the practice of training video understanding models. Due to the overhead of setting up the proposed framework (even though quite simple), adoption by the community may be fairly limited. Final rating: - After having read the other reviews and the author responses, I decide to maintain my initial rating (6). The contribution of this work is mostly empirical. The stronger results compared to more complex models and the promise to release the code imply that this work deserves to be known, even if fairly incremental.	- After having read the other reviews and the author responses, I decide to maintain my initial rating (6). The contribution of this work is mostly empirical. The stronger results compared to more complex models and the promise to release the code imply that this work deserves to be known, even if fairly incremental.
NIPS_2018_245	NIPS_2018	Weakness] 1: Poor writing and annotations are a little hard to follow. 2: Although applying GCN on FVQA is interesting, the technical novelty of this paper is limited. 3: The motivation is to solve when the question doesn't focus on the most obvious visual concept when there are synonyms and homographs. However, from the experiment, it's hard to see whether this specific problem is solved or not. Although the number is better than the previous method, it will be great if the authors could product more experiments to show more about the question/motivation raised in the introduction. 4: Following 3, applying MLP after GCN is very common, and I'm not surprised that the performance will drop without MLP. The authors should show more ablation studies on performance when varying the number of facts retrieval, what happened if we different number of layer of GCN?	1: Poor writing and annotations are a little hard to follow.
WC9yjSosSA	EMNLP_2023	- The reported experimental results cannot strongly demonstrate the effectiveness of the proposed method. - In Table 1, for the proposed method, only 6 of the total 14 evaluation metrics achieve SOTA performances. - In Table 2, for the proposed method, only 8 of the total 14 evaluation metrics achieve SOTA performances. In addition, under the setting of "Twitter-2017 $\rightarrow$ Twitter-2015", why the proposed method achieves best overall F1, while not achieves best F1 in all single types? - In Table 3, for the proposed method, 9 of the total 14 evaluation metrics achieve SOTA performances, which means that when ablating some modules, the performance of the proposed method will improve. Furthermore, The performance improvement that adding a certain module can bring is not obvious. - In line 284, a transformer layer with self-attention is used to capture the intra-modality relation for the test modality. However, there're a lot of self-attention transformer layers in BERT. Why not using the attention scores in the last self-attention transformer layer? - In line 322, softmmax -> softmax - Will the coordination of $b_d$ exceed the scope of the patches?	- In Table 2, for the proposed method, only 8 of the total 14 evaluation metrics achieve SOTA performances. In addition, under the setting of "Twitter-2017 $\rightarrow$ Twitter-2015", why the proposed method achieves best overall F1, while not achieves best F1 in all single types?
ICLR_2022_2531	ICLR_2022	I have several concerns about the clinical utility of this task as well as the evaluation approach. - First of all, I think clarification is needed to describe the utility of the task setup. Why is the task framed as generation of the ECG report rather than framing the task as multi-label classification or slot-filling, especially given the known faithfulness issues with text generation? There are some existing approaches for automatic ECG interpretation. How does this work fit into the existing approaches? A portion of the ECG reports from the PTB-XL dataset are actually automatically generated (See Data Acquisition under https://physionet.org/content/ptb-xl/1.0.1/). Do you filter out those notes during evaluation? How does your method compare to those automatically generated reports? - A major claim in the paper is that RTLP generates more clinically accurate reports than MLM, yet the only analysis in the paper related to this is a qualitative analysis of a single report. A more systematic analysis of the quality of generation would be useful to support the claim made in the appendix. Can you ask clinicians to evaluate the utility of the generated reports or evaluate clinical utility by using the generated reports to predict conditions identifiable from the ECG? I think that it’s fine that the RTLP method performs comparable to existing methods, but I am not sure from the current paper what the utility of using RTLP is. - More generally, I think that this paper is trying to do two things at once – present new methods for multilingual pretraining while also developing a method of ECG captioning. If the emphasis is on the former, then I would expect to see evaluation against other multilingual pretraining setups such as the Unicoder (Huang 2019a). If the core contribution is the latter, then clinical utility of the method as well as comparison to baselines for ECG captioning (or similar methods) is especially important. - I’m a bit confused as to why the diversity of the generated reports is emphasized during evaluation. While I agree that the generated reports should be faithful to the associated ECG, diversity may not actually be necessary metric to aim for in a medical context. For instance, if many of the reports are normal, you would want similar reports for each normal ECG (i.e. low diversity). - My understanding is that reports are generated in other languages using Google Translate. While this makes sense to generate multilingual reports for training, it seems a bit strange to then evaluate your model performance on these silver-standard noisy reports. Do you have a held out set of gold standard reports in different languages for evaluation (other than German)? Other Comments: - Why do you only consider ECG segments with one label assigned to them? I would expect that the associated reports would be significantly easier than including all reports. - You might consider changing the terminology from “cardiac arrythmia” categories to something broader since hypertrophy (one of the categories) is not technically a cardiac arrythmia (although it can be detected via ECG & it does predispose you to them) - I think it’d be helpful to include an example of some of the tokens that are sampled during pretraining using your semantically similar strategy for selecting target tokens. How well does this work in languages that have very different syntactic structures compared to the source language? - Do you pretrain the cardiac signal representation learning model on the entire dataset or just the training set? If the entire set, how well does this generalize to setting where you don’t have the associated labels? - What kind of tokenization is used in the model? Which Spacy tokenizer? - It’d be helpful to reference the appendix when describing the setup in section 3/5 so that the reader knows that more detailed architecture information is there. - I’d be interested to know if other multilingual pretraining setups also struggle with Greek. - It’d be helpful to show the original ECG report with punctuation + make the ECG larger so that they are easier to read - Why do you think RTLP benefits from fine-tuning on multiple languages, but MARGE does not?	- Why do you only consider ECG segments with one label assigned to them? I would expect that the associated reports would be significantly easier than including all reports.
NIPS_2016_238	NIPS_2016	- My biggest concern with this paper is the fact that it motivates âdiversityâ extensively (even the word diversity is in the title) but the model does not enforce diversity explicitly. I was all excited to see how the authors managed to get the diversity term into their model and got disappointed when I learned that there is no diversity. - The proposed solution is an incremental step considering the relaxation proposed by Guzman. et. al. Minor suggestions: - The first sentence of the abstract needs to be re-written. - Diversity should be toned down. - line 108, the first âfâ should be âgâ in âwe fixed the form of ..â - extra â.â in the middle of a sentence in line 115. One Question: For the baseline MCL with deep learning, how did the author ensure that each of the networks have converged to a reasonable results. Cutting the learners early on might significantly affect the ensemble performance.	- The proposed solution is an incremental step considering the relaxation proposed by Guzman. et. al. Minor suggestions:
pZk9cUu8p6	ICLR_2025	1.Limited Discussion of Scalability Bounds:The paper doesn't thoroughly explore the upper limits of FedDES's scalability；No clear discussion of memory requirements or computational complexity. 2.Validation Scope:Evaluation focuses mainly on Vision Transformer with CIFAR-10;Could benefit from testing with more diverse models and datasets; Limited exploration of edge cases or failure scenarios 3.Network Modeling:While network delays are considered, there's limited discussion of complex network topologies or dynamic network conditions; The paper could benefit from more detailed analysis of how network conditions affect simulation accuracy	1.Limited Discussion of Scalability Bounds:The paper doesn't thoroughly explore the upper limits of FedDES's scalability；No clear discussion of memory requirements or computational complexity.
fsDZwS49uY	ICLR_2025	- The authors may want to generate instances with more constraints and variables, as few instances in the paper have more than 7 variables. Thus, this raises my concern about LLMs' ability to model problems with large instance sizes. - Given that a single optimization problem can have multiple valid formulations, it would be beneficial for the authors to verify the accuracy and equivalence of these formulations with ground-truth ones. - There are questions regarding the solving efficiency of the generated codes. It would be valuable to assess whether the code produced by LLMs can outperform human-designed formulations and codes.	- The authors may want to generate instances with more constraints and variables, as few instances in the paper have more than 7 variables. Thus, this raises my concern about LLMs' ability to model problems with large instance sizes.
RsnWEcuymH	ICLR_2024	- My main concern is that the performance improvement, though generally better, is not particularly too significant, not to mention that those proxy-based method achieves also pretty good IM results while using only a negligible amount of time compared to BOIM (or other simulation-based method in general) - Other choices of graph kernel are not considered and experimented with such as random walk or Graphlet kernel? There are probably easy tricks to turn them into valid GP kernels. - Despite the time reduction introduced by BOIM, proxy-based methods are still substantially cheaper. Would it be possible to use proxy-based methods as heuristics to seed BOIM or other zero-order optimization method (e.g., CMA-ES). - While the author has shown that GSS has theoretically lower variance, it’d be nice to compare against with random sampling and check empirically how well it performs. - Results presentation can be improved. For example, in Figure 2 and 3, the y-axis is labeled as “performance” which is ambiguous, and the runtime is not represented in those figure. A scatter plot with x/y axes being runtime/performance could help the reader better understand and interpret the results. Best results in tables can also be highlighted. Minor: - Typo in Section 2: “Mockus (1998) and has since become…” → “Mockus (1998) has since become…”	- Results presentation can be improved. For example, in Figure 2 and 3, the y-axis is labeled as “performance” which is ambiguous, and the runtime is not represented in those figure. A scatter plot with x/y axes being runtime/performance could help the reader better understand and interpret the results. Best results in tables can also be highlighted. Minor:
ICLR_2023_3063	ICLR_2023	The novelty and technical contribution are limited. It is unclear for the deformable graph attention module. It is unclear why the proposed method has lower computational complexity. Detailed comments: What is the motivation to choose personalized pagerank score, bfs, and feature similarity as sorting criteria? For NodeSort, 1) how to choose the base node, or is every node a base node? 2)“NodeSort differentially sorts nodes depending on the base node.” Does this mean that the base node affects the ordering, affects the key nodes for attention, and further affects the model performance? 3)After getting the sorted node sequence, how to sample the key nodes for each node? And how many key nodes are sampled？is the number of key nodes a hyper-parameter? 4)What are the Value nodes used in Transformer in this paper? 5)How to fuse node representations generated by attention for different ranking criteria. Intuitively, the design of deformable graph attention is complicated, and the Katz positional encoding involves the exponentiation of adjacency matrix, so Is the computational complexity really reduced? Where can the reduction in complexity be explained from the proposed method compared to baselines? or just from the sparse implementation?	2)“NodeSort differentially sorts nodes depending on the base node.” Does this mean that the base node affects the ordering, affects the key nodes for attention, and further affects the model performance?
ICLR_2022_176	ICLR_2022	There are two main (and easily fixable) weaknesses. a) I think the role of the normalizing flow is underexplained. It is stated multiple times that the normalizing flow provides the evidence updates and its purpose is to estimate epistemic uncertainty. The remaining questions for me are 1. From which space to which does the NF map the latent variable z? 2. Why is the arrow in Figure 2 from a Gaussian space into the latent space, rather than from the latent space to n^(i)? I thought the main purpose was to influence n^(i)? 3. Which experiments show that the normalizing flow contributes meaningfully to the epistemic uncertainty (see b))? b) Figure 1 does a good job of showing the intuition behind NatPNs but it lacks some components and a discussion in the text. The authors choose to show aleatoric (un-)certainty and predictive certainty respectively but don’t show epistemic (un-)certainty. Technically, you could deduce epistemic uncertainty from aleatoric and predictive uncertainty but it would be easier to compare and follow your argument if it was made explicit. Furthermore, I would like to see an explicit discussion of the results. Why, for example, is the difference between aleatoric and predictive uncertainty so low? Is there no or little epistemic uncertainty in this setting? There are two things that would convince me more regarding this problem: a) an additional toy experiment similar to Figure 1 which includes more epistemic uncertainty, e.g. with fewer data points. This could show that the epistemic uncertainty is well-calibrated. b) An argument for why the epistemic uncertainty is (presumably) so low in your setting. a) and b) are not mutually exclusive, doing both would convince me more. There are a couple of minor improvements: Figure 1 is not referenced in the main text. I find it hard to spot the difference w.r.t the symbols in Figure 1. Maybe just making it less crowded would already improve visibility. In the last paragraph of 3.1, you mention “warm-up” and “fine-tuning”. It would be helpful to explain these concepts briefly in one additional sentence or provide references. What would raise my score? I would raise my score by 1 or 2 points if my main weaknesses are well addressed or if evidence is provided that my criticism is the consequence of a misunderstanding. I would raise my score even further if I’m convinced of the high significance of this work. This will be mostly dependent on the estimate of more expert reviewers but I’m also open to arguments by the authors.	2. Why is the arrow in Figure 2 from a Gaussian space into the latent space, rather than from the latent space to n^(i)? I thought the main purpose was to influence n^(i)?
ICLR_2023_1946	ICLR_2023	Weakness: 1. This work raises an essential issue in partial domain adaptation and evaluates many PDA algorithms and model selection strategies. However, it does not present any solution to this problem. 2. The findings of the experiments are a bit trivial. No target label for model selection strategies will hurt the performance, and the random seed would influence the performance. They are commonsense in domain adaptation, even in deep learning. 3. The writing needs to improve. The Tables are referenced but always placed on different pages. For example, Table 2 is referred in page 4 but placed on page 3, making it hard to read. The paper also has many typos, e.g., ‘that’ instead of ‘than’ in section 3. 4. Many abbreviations lack definition and cause confusion. ‘AR’ in Table 5 stands for domain adaptation tasks and algorithms. 5. In section 4.2, heuristic strategies for Hyper-parameter turning is not clearly described. And the author said, “we only consider the model at the end of training”, but should we use the model selection strategies? 6. In section 5.2, part of Model Selection Strategies, the authors give a conclusion that seems to be wrong “only the JUMBOT and SND pair performed reasonably well with respect to the JUMBOT and ORACLE pair on both datasets.” In Table 6, the JUMBOT and SND pair performs worse than the JUMBOT and ORACLE pair by a large margin. For instance, on OFFICE-HOME, the JUMBOT and SND pair reaches 72.29 accuracies, while the JUMBOT and ORACLE pair achieves 77.15 accuracies.	4. Many abbreviations lack definition and cause confusion. ‘AR’ in Table 5 stands for domain adaptation tasks and algorithms.
NIPS_2020_1477	NIPS_2020	1. I think it is a bit overstated (Line 10 and Line 673) to use the term \epsilon-approximate stationary point of J -- there is still function approximation error as in Theorem 4.5. I think the existence of this function approximation error should be explicitly acknowledged whenever the conclusion about sample complexity is stated. Otherwise readers may have the impression that compatible features (Konda, 2002, Sutton et al 2000) are used to deal with these errors, which are not the case. 2. As shown by Konda (2002) and Sutton et al (2000), compatible features are useful tools to address the function approximation error of the critic. I'm wondering if it's possible to introduce compatible features and TD(1) critic in the finite sample complexity analysis in this paper to eliminate the \epsilon_app term. 3. I feel the analysis in the paper depends heavily on the property of the stationary distribution (e.g., Line 757). I'm wondering if it's possible to conduct a similar analysis for the discounted setting (instead of the average reward setting). Although a discounted problem can be solved by methods for the average reward problem (e.g., discarding each transition w.p. 1 - \gamma, see Konda 2002), solving the discounted problem directly is more common in the RL community. It would be beneficial to have a discussion w.r.t. the discounted objective. 4. Although using advantage instead of q value is more common in practice, I'm wondering if there is other technical consideration for conducting the analysis with advantage instead of q value. 5. The assumption about maximum eigenvalues in Line 215 seems artificial. I can understand this assumption, as well as the projection in Line 8 in Algorithm 1, is mainly used to ensure the boundedness of the critic. However, as in Line 219, R_w indeed depends on \lambda, which we do not know in practice. So it means we cannot implement the exact Algorithm 1 in practice. Instead of using this assumption and the projection, is it possible to use regularization (e.g., ridge) for the critic to ensure it's bounded, as done in asymptotic analysis in Zhang et al (2020)? Also Line 216 is a bit misleading. Only the first half (negative definiteness) is used to ensure solvability. But as far as I know, in policy evaluation setting, we do not need the second half (maximum eigenvalue). 6. Some typos: Line 463 should include \epsilon_app and replace the first + with \leq \epsilon_app (the last term of Line 587) is missing in Line 585 and 586 There shouldn't be (1 - \gamma) in Line 589 In Line 618, there should be no need to introduce the summation from k=0 to t - \tau_t, as the summation from k=\tau_t to t is still used in Line 624. In Line 625, it should be \tau_t instead of \tau In Line 640, I personally think it's not proper to cite [25] (the S & B book) -- that book includes too many. Referring to the definition of w^* should be more easy to follow. In Line 658, it should be \|\|z_k\|\|^2 In Line 672, \epsilon_app is missing In Line 692, it should be E[....] = 0 In Line 708, there shouldn't be \theta_1, \theta_2, \eta_1, \eta_2 In Line 774, I think expectation is missing in the LHS Konda, V. R. Actor-critic algorithms. PhD thesis, Massachusetts Institute of Technology, 2002. Zhang, S., Liu, B., Yao, H., & Whiteson. Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation. ICML 2020.	4. Although using advantage instead of q value is more common in practice, I'm wondering if there is other technical consideration for conducting the analysis with advantage instead of q value.
jPrl18r4RA	EMNLP_2023	1. The setting of Unsupervised Online Adaptation is a little bit strange. As described in Sec 3.1, the model requires a training set, including documents, quires and labels. It seems that the adaptation process is NOT "Unsupervised" because the training set also requires annatations. 2. The problem that this paper focuses on may be unrealistic. Figure 8 shows that adapting to test documents leads to performance degradation on unrelated queries. In practice, we expect to update the knowledge of Large Language Models without affecting the performance on general tasks. Besides, existing large scale QA systems, e.g. GPT-4, show strong In-Context Learning abilities. In other words, the model can reason about new documents and answer questions that have never been seen before.	1. The setting of Unsupervised Online Adaptation is a little bit strange. As described in Sec 3.1, the model requires a training set, including documents, quires and labels. It seems that the adaptation process is NOT "Unsupervised" because the training set also requires annatations.
ICLR_2022_1923	ICLR_2022	Weakness: 1. The novelty of this paper is limited. First, the analysis of the vertex-level imbalance problem is not new, which is a reformulation of the observations in previous works [Rendle and Freudenthaler, 2014; Ding et al., 2019]. Second, the designed negative sampler uses reject sampling to increase the chance of popular items, which is similar to the proposed one in PRIS [Lian et al., 2020]. 2. The paper overclaims on its ability of debiasing sampling. The “debiased” term in the paper title is confusing. 3. The methodology detail is unclear in Sec. 4.2. The proposed design that improves sampling efficiency seems interesting but the corresponding description is hard to follow given the limited space. 4. Space complexity of the proposed VINS should also be analyzed and compared in empirical studies, given that each (u, i) corresponds to a b u f f e r u i . 5. Experiment results are not convincing enough to demonstrate the superiority of VINS on effectiveness and efficiency. - For effectiveness, the performance comparison in Table 1 is unfair. VINS sets different sample weights W u i in the training process, while most compared baselines like DNS, AOBPR, SA, PRIS set all sample weights as 1. - For efficiency, Table 2 should also include the theoretical analysis for contrast.	- For effectiveness, the performance comparison in Table 1 is unfair. VINS sets different sample weights W u i in the training process, while most compared baselines like DNS, AOBPR, SA, PRIS set all sample weights as 1.
NIPS_2019_1366	NIPS_2019	Weakness: - Although the method discussed by the paper can be applied in general MDP, the paper is limited in navigation problems. Combining RL and planning has already been discussed in PRM-RL~[1]. It would be interesting whether we can apply such algorithms in more general tasks. - The paper has shown that pure RL algorithm (HER) failed to generalize to distance goals but the paper doesn't discuss why it failed and why planning can solve the problem that HER can't solve. Ideally, if the neural networks are large enough and are trained with enough time, Q-Learning should converge to not so bad policy. It will be better if the authors can discuss the advantages of planning over pure Q-learning. - The time complexity will be too high if the reply buffer is too large. [1] PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning	- The time complexity will be too high if the reply buffer is too large. [1] PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
NIPS_2022_1807	NIPS_2022	Weakness: 1.The authors should provide more descriptions of the wavelet transforms in this paper. It is hard for me to understand the major idea in this paper before learning some necessary knowledge about wavelet whitening, wavelet coefficient, and so on. 2.It is better for authors to display the performance of accelerating SGMs by involving some other baselines with a different perspective, such as “optimizing the discretization schedule or by modifying the original SGM formulation” [16, 15, 23, 46, 36, 31, 37, 20, 10, 25, 35, 45]	2.It is better for authors to display the performance of accelerating SGMs by involving some other baselines with a different perspective, such as “optimizing the discretization schedule or by modifying the original SGM formulation” [16, 15, 23, 46, 36, 31, 37, 20, 10, 25, 35, 45]
ICLR_2021_243	ICLR_2021	Weakness: 1. As several modifications mentioned in Section 3.4 were used, it would be better to provide some ablation experiments of these tricks to validate the model performance further. 2. The model involves many hyperparameters. Thus, the selection of the hyperparameters in the paper needs further explanation. 3. A brief conclusion of the article and a summary of this paper's contributions need to be provided. 4. Approaches that leveraging noisy label noise label regularization and multi-label co-regularization were not reviewed or compared in this paper.	3. A brief conclusion of the article and a summary of this paper's contributions need to be provided.
ICLR_2022_1838	ICLR_2022	1. When introducing the theoretical results, we should make a detailed comparison with the existing cross-entropy loss results. The current writing method cannot reflect the advantages of square loss. 2. The synthetic experiment in a non-separable case seems to be a problem. Considering the nonlinear expression ability of neural networks, how to explain that the data distribution illustrated in Figure 1 is inseparable from the network model? 3. This paper presents that the loss functions like hinge loss don’t provide reliable information on the prediction confidence. In this regard, there is a lack of references to some relevant literature. [Gao, 2013] has given a detailed analysis of the advantages and disadvantages between the entire margin distribution and the minimum margin. Based on this, [Lyu, 2018] designed a square-type margin distribution loss to improve the generalization ability of DNN. [Gao, 2013] W. Gao and Z.-H. Zhou. On the doubt about margin explanation of boosting. Artificial Intelligence 203:1-18 2013. [Lyu, 2018] Shen-Huan Lyu, Lu Wang, and Zhi-Hua Zhou. Improving Generalization of Neural Networks by Leveraging Margin Distribution. http://arxiv.org/abs/1812.10761	2. The synthetic experiment in a non-separable case seems to be a problem. Considering the nonlinear expression ability of neural networks, how to explain that the data distribution illustrated in Figure 1 is inseparable from the network model?
ICLR_2021_2717	ICLR_2021	1: The writing could be further improved, e.g., “via being matched to” should be “via matching to” in Abstract. 2: The “Def-adv” needs to be clarified. 3: The accuracies of the target model using different defenses against the FGSM attack are not shown in Figure 1. Hence, it is unclear the difference between the known attacks and the unknown attacks. 4: Even though authors compare their framework with an advanced defense APE-GAN, they can further compare the proposed framework with a method that is designed to defend against multiple attacks (maybe the research on defense against multiple attacks is relatively rare). The results would be more meaningful if the authors could present this comparison in their paper. Overall the paper presents an interesting study that would be useful for defending the threat of increasing malicious perturbations.	4: Even though authors compare their framework with an advanced defense APE-GAN, they can further compare the proposed framework with a method that is designed to defend against multiple attacks (maybe the research on defense against multiple attacks is relatively rare). The results would be more meaningful if the authors could present this comparison in their paper. Overall the paper presents an interesting study that would be useful for defending the threat of increasing malicious perturbations.
82VzAtBZGk	ICLR_2025	The problem formulation is incomplete. The paper does not define the safety properties expected from the RL agent. - Lack of theoretical results. This paper provides only empirical results to support its claims. - The results are presented in a convoluted way. In particular, the results disregard the safety violations of the agent in the first 1000 episodes. The reason for presenting the results in this way is unclear. - The presentation of the DDPG-Lag as a constrained RL algorithm is imprecise, as it uses a fixed weight for the costs, which works as simple reward engineering. In general, with a Lagrangian relaxation, this weight should be adjusted online to ensure the accumulated cost stays below a predefined threshold [1]. - The evaluation in CMDPs is inconsistent. These approaches solve different problems where a predefined accumulated cost is allowed. - Weak baseline. From the results in Figure 10, it is clear that Tabular Shield does not recognize any unsafe state-action pairs, making it an unsuitable baseline. This is not surprising considering how the state-action space is discretized. Perhaps it is necessary to finetune the discretization of this baseline. Alternatively, it would be more suitable to consider stronger baselines, such as the accumulating safety rules [2] references - [1] Ray, A., Achiam, J., and Amodei, D. (2019). Benchmarking safe exploration in deep reinforcement learning. <https://github.com/openai/safety-gym> - [2] Shperberg, S. S., Liu, B., Allievi, A., and Stone, P. (2022). A rule-based shield: Accumulating safety rules from catastrophic action effects. CoLLAs, 231–242. <https://proceedings.mlr.press/v199/shperberg22a.html>	- The results are presented in a convoluted way. In particular, the results disregard the safety violations of the agent in the first 1000 episodes. The reason for presenting the results in this way is unclear.