2 citations · 9 across the 46 of their papers we have counts for
4 papers · 2 filters
Efficient Conformal Prediction under Data Heterogeneity
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii +7
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods…
Model-free Posterior Sampling via Learning Rate Randomization
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +6
In this paper, we introduce Randomized Q-learning (RandQL), a novel randomized model-free algorithm for regret minimization in episodic Markov Decision Processes (MDPs). To the bes…
Demonstration-Regularized RL
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +5
Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this…
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
Sergey Samsonov, Daniil Tiapkin, Alexey Naumov +1
In this paper we consider the problem of obtaining sharp bounds for the performance of temporal difference (TD) methods with linear function approximation for policy evaluation in…