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Found 10 papers, 0 papers with code
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes
To this end, we posit a joint model class for tasks and use the notion of $\eta$-bracketing number to quantify its complexity; this number also serves as a general metric to capture the similarity of tasks and thus determines the benefit of multi-task over single-task RL.
Temporal-Distributed Backdoor Attack Against Video Based Action Recognition
Although there are extensive studies on backdoor attacks against image data, the susceptibility of video-based systems under backdoor attacks remains largely unexplored.
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
The general sequential decision-making problem, which includes Markov decision processes (MDPs) and partially observable MDPs (POMDPs) as special cases, aims at maximizing a cumulative reward by making a sequence of decisions based on a history of observations and actions over time.
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
A new FL convergence bound is derived which, combined with the privacy guarantees, allows for a smooth tradeoff between the achieved convergence rate and differential privacy levels.
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
This paper investigates conservative exploration in reinforcement learning where the performance of the learning agent is guaranteed to be above a certain threshold throughout the learning process.
Federated Linear Contextual Bandits with User-level Differential Privacy
This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP).
Non-stationary Reinforcement Learning under General Function Approximation
To the best of our knowledge, this is the first dynamic regret analysis in non-stationary MDPs with general function approximation.
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs
In this work, we first provide the first known sample complexity lower bound that holds for any algorithm under low-rank MDPs.
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL
In particular, we consider the scenario where a safe baseline policy is known beforehand, and propose a unified Safe reWard-frEe ExploraTion (SWEET) framework.
Federated Linear Contextual Bandits
This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters.
