67 citations · 145 across the 35 of their papers we have counts for
8 papers · 1 filter
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
Jimmy Z. Di, Jack Douglas, Jayadev Acharya +2
We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An ad…
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
Dylan J. Foster, Noah Golowich, Jian Qian +2
We consider the problem of interactive decision making, encompassing structured bandits and reinforcement learning with general function approximation. Recently, Foster et al. (202…
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Satyen Kale, Jason D. Lee, Chris De Sa +2
Stochastic Gradient Descent (SGD) has been the method of choice for learning large-scale non-convex models. While a general analysis of when SGD works has been elusive, there has b…
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
Yuda Song, Yifei Zhou, Ayush Sekhari +3
We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online inte…
On the Complexity of Adversarial Decision Making
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari +1
A central problem in online learning and decision making -- from bandits to reinforcement learning -- is to understand what modeling assumptions lead to sample-efficient learning g…
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee +2
We study reinforcement learning with function approximation for large-scale Partially Observable Markov Decision Processes (POMDPs) where the state space and observation space are…
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee +2
We study Reinforcement Learning for partially observable dynamical systems using function approximation. We propose a new \textit{Partially Observable Bilinear Actor-Critic framewo…
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Christoph Dann, Yishay Mansour, Mehryar Mohri +2
Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to explore efficiently in some reinforcement learning tasks and yet, they perform well in many o…