1 citations · 3 across the 13 of their papers we have counts for
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Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates
Sreejeet Maity, Aritra Mitra
We study the problem of learning the optimal policy in a discounted, infinite-horizon reinforcement learning (RL) setting in the presence of adversarially corrupted rewards. To add…
Boosting-Enabled Robust System Identification of Partially Observed LTI Systems Under Heavy-Tailed Noise
Vinay Kanakeri, Aritra Mitra
We consider the problem of system identification of partially observed linear time-invariant (LTI) systems. Given input-output data, we provide non-asymptotic guarantees for identi…
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
Feng Zhu, Aritra Mitra, Robert W. Heath
Motivated by collaborative reinforcement learning (RL) and optimization with time-correlated data, we study a generic federated stochastic approximation problem involving agent…
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits
Sreejeet Maity, Aritra Mitra
One of the most basic problems in reinforcement learning (RL) is policy evaluation: estimating the long-term return, i.e., value function, corresponding to a given fixed policy. Th…
Outlier-Robust Linear System Identification Under Heavy-tailed Noise
Vinay Kanakeri, Aritra Mitra
We consider the problem of estimating the state transition matrix of a linear time-invariant (LTI) system, given access to multiple independent trajectories sampled from the system…