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06/05/2023
Data-Driven Regret Balancing for Online Model Selection in Bandits
We consider model selection for sequential decision making in stochastic...
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02/20/2023
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond
Best-of-both-worlds algorithms for online learning which achieve near-op...
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02/18/2023
Best of Both Worlds Policy Optimization
Policy optimization methods are popular reinforcement learning algorithm...
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02/03/2023
Pseudonorm Approachability and Applications to Regret Minimization
Blackwell's celebrated approachability theory provides a general framewo...
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10/17/2022
A Unified Algorithm for Stochastic Path Problems
We study reinforcement learning in stochastic path (SP) problems. The go...
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06/29/2022
Best of Both Worlds Model Selection
We study the problem of model selection in bandit scenarios in the prese...
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06/19/2022
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian...
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02/21/2022
Same Cause; Different Effects in the Brain
To study information processing in the brain, neuroscientists manipulate...
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10/07/2021
A Model Selection Approach for Corruption Robust Reinforcement Learning
We develop a model selection approach to tackle reinforcement learning w...
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07/02/2021
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
We provide improved gap-dependent regret bounds for reinforcement learni...
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06/22/2021
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
There have been many recent advances on provably efficient Reinforcement...
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06/06/2021
Neural Active Learning with Performance Guarantees
We investigate the problem of active learning in the streaming setting i...
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12/24/2020
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL
We propose a simple model selection approach for algorithms in stochasti...
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05/07/2020
Reinforcement Learning with Feedback Graphs
We study episodic reinforcement learning in Markov decision processes wh...
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11/05/2019
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy
While maximizing expected return is the goal in most reinforcement learn...
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11/07/2018
Policy Certificates: Towards Accountable Reinforcement Learning
The performance of a reinforcement learning algorithm can vary drastical...
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03/01/2018
On Polynomial Time PAC Reinforcement Learning with Rich Observations
We study the computational tractability of provably sample-efficient (PA...
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06/09/2017
Decoupling Learning Rules from Representations
In the artificial intelligence field, learning often corresponds to chan...
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03/22/2017
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Statistical performance bounds for reinforcement learning (RL) algorithm...
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02/21/2017
Sample Efficient Policy Search for Optimal Stopping Domains
Optimal stopping problems consider the question of deciding when to stop...
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11/21/2016
Memory Lens: How Much Memory Does an Agent Use?
We propose a new method to study the internal memory used by reinforceme...
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11/05/2015
Thoughts on Massively Scalable Gaussian Processes
We introduce a framework and early results for massively scalable Gaussi...
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10/29/2015
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning
Recently, there has been significant progress in understanding reinforce...
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10/26/2015
The Human Kernel
Bayesian nonparametric models, such as Gaussian processes, provide a com...
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07/22/2015