43 citations · 46 across the 5 of their papers we have counts for
5 papers
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári +1
This paper introduces a simple efficient learning algorithms for general sequential decision making. The algorithm combines Optimism for exploration with Maximum Likelihood Estimat…
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
Han Zhong, Zhongren Chen, Zhuoran Yang +2
We study episodic reinforcement learning (RL) in non-stationary linear kernel Markov decision processes (MDPs). In this setting, both the reward function and the transition kernel…
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions
Gellért Weisz, Csaba Szepesvári, András György
We consider the minimax query complexity of online planning with a generative model in fixed-horizon Markov decision processes (MDPs) with linear function approximation. Following…
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
Jonathan Uesato, Ananya Kumar, Csaba Szepesvari +7
This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus o…
Alignment Based Kernel Learning with a Continuous Set of Base Kernels
Arash Afkanpour, Csaba Szepesvari, Michael Bowling
The success of kernel-based learning methods depend on the choice of kernel. Recently, kernel learning methods have been proposed that use data to select the most appropriate kerne…