13 papers
Finite-Time Analysis of Discounted Exponential-Utility Reinforcement Learning
Ankur Naskar, Vivek T A, Aditya Kumar +2
Discounted exponential utility provides a principled criterion for risk-sensitive sequential decision-making, but its nonlinear structure complicates reinforcement learning. A rece…
Robustness to Sparse Adversarial Corruption in Arbitrary Linear Measurements: Beyond Exact Recovery
Vishal Halder, Alexandre Reiffers-Masson, Abdeldjalil Aïssa-El-Bey +1
Recovery from linear measurements under sparse adversarial corruption is typically formulated as an exact-recovery problem: one seeks structural conditions on (e.g., r…
Adversary-Robust Learning from Fully Asynchronous Directional Derivative Estimates
Anik Kumar Paul, Nibedita Roy, Nagesh Talagani +3
We propose FAR-SIGN (Fully Asynchronous Robust optimization via SIGNed directional projections) for adversary-resilient learning in parameter-server--worker systems. FAR-SIGN achie…
Reinforcement Learning for Exponential Utility: Algorithms and Convergence in Discounted MDPs
Gugan Thoppe, L. A. Prashanth, Ankur Naskar +1
Reinforcement learning (RL) for exponential-utility optimization in discounted Markov decision processes (MDPs) lacks principled value-based algorithms. We address this gap in the…
End-to-End and Phase-Level Performance Optimization for Hyperledger Fabric
Pavan Sollu, Aniruddha Mukherjee, Divya Pulivarthi +6
Hyperledger Fabric (HLF) is a modular, permissioned blockchain widely adopted in enterprise settings. Enhancing its throughput and latency remains challenging, as optimization deci…
Tight Convergence Rates for Online Distributed Linear Estimation with Adversarial Measurements
Nibedita Roy, Vishal Halder, Gugan Thoppe +4
We study mean estimation of a random vector in a distributed parameter-server-worker setup. Worker observes samples of , where is the th row of a…