in the bibliography, switching-bandit algorithms with dependencies on the number of switches (EXP3.S is however cited[7]) and gaps between action's mean rewards being not described (Discounted UCB, Sliding Window UCB, EXP3 with reset). The detector of non-stationary has also some similarities with the SAO (Stochastic and Adversarial Optimal) algorithm that starts with a stationary algorithm and then switches to EXP3 if a non-stationarity is detected. - The proposed algorithm is well constructed and is a step-forward from the main reference [1]. While not having a major practical impact (has an high regret in stationary regime and in highly non-stationary regime, even if the best action does not change) it resolves and open problem and gives some closure the the analysis using the variation. - After the post rebuttal discussion, we agreed to rank this paper at poster level (5: 4->3).
- The proposed algorithm is well constructed and is a step-forward from the main reference [1]. While not having a major practical impact (has an high regret in stationary regime and in highly non-stationary regime, even if the best action does not change) it resolves and open problem and gives some closure the the analysis using the variation.