Siddharth Chandak

According to our database, Siddharth Chandak authored at least 22 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Policy Gradient Methods for Non-Markovian Reinforcement Learning.

Avik Kar, Siddharth Chandak, Rahul Singh, Soumitra Sinhahajari, Eric Moulines, Shalabh Bhatnagar, Nicholas Bambos

CoRR, May, 2026

Last-Iterate Guarantees for Learning in Co-coercive Games.

Siddharth Chandak, Ramanan Tamizholi, Nicholas Bambos

CoRR, April, 2026

Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis.

Siddharth Chandak, Anuj Yadav, Ayfer Özgür, Nicholas Bambos

CoRR, March, 2026

High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise.

Avik Kar, Siddharth Chandak, Rahul Singh, Eric Moulines, Shalabh Bhatnagar, Nicholas Bambos

CoRR, March, 2026

Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains.

Rahul Singh, Siddharth Chandak, Eric Moulines, Vivek S. Borkar, Nicholas Bambos

CoRR, February, 2026

2025

Choose Your Battles: Distributed Learning Over Multiple Tug of War Games.

Siddharth Chandak, Ilai Bistritz, Nicholas Bambos

CoRR, September, 2025

O(1/k) Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation.

Siddharth Chandak

CoRR, April, 2025

Non-Expansive Mappings in Two-Time-Scale Stochastic Approximation: Finite-Time Analysis.

Siddharth Chandak

CoRR, January, 2025

Optimal Control for Remote Patient Monitoring with Multidimensional Health States.

Siddharth Chandak, Isha Thapa, Nicholas Bambos, David Scheinker

Proceedings of the IEEE International Conference on Communications, 2025

Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise.

Siddharth Chandak, Shaan Ul Haque, Nicholas Bambos

Proceedings of the 64th IEEE Conference on Decision and Control, 2025

2024

Reinforcement learning in non-Markovian environments.

Siddharth Chandak, Pratik Shah, Vivek S. Borkar, Parth Dodhia

Syst. Control. Lett., 2024

Learning to Control Unknown Strongly Monotone Games.

Siddharth Chandak, Ilai Bistritz, Nicholas Bambos

CoRR, 2024

Tiered Service Architecture for Remote Patient Monitoring.

Siddharth Chandak, Isha Thapa, Nicholas Bambos, David Scheinker

Proceedings of the IEEE International Conference on E-health Networking, 2024

2023

A concentration bound for LSPE(λ).

Siddharth Chandak, Vivek S. Borkar, Harsh Dolhare

Syst. Control. Lett., 2023

A Concentration Bound for TD(0) with Function Approximation.

Siddharth Chandak, Vivek S. Borkar

CoRR, 2023

Tug of Peace: Distributed Learning for Quality of Service Guarantees.

Siddharth Chandak, Ilai Bistritz, Nicholas Bambos

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Equilibrium Bandits: Learning Optimal Equilibria of Unknown Dynamics.

Siddharth Chandak, Ilai Bistritz, Nicholas Bambos

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message.

Shaan ul Haque, Siddharth Chandak, Federico Chiariotti, Deniz Gündüz, Petar Popovski

IEEE Commun. Lett., 2022

Reinforcement Learning in Non-Markovian Environments.

Siddharth Chandak, Vivek S. Borkar, Parth Dodhia

CoRR, 2022

2021

Prospect-theoretic Q-learning.

Vivek S. Borkar, Siddharth Chandak

Syst. Control. Lett., 2021

Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources.

Siddharth Chandak, Federico Chiariotti, Petar Popovski

IEEE Commun. Lett., 2021

Concentration of Contractive Stochastic Approximation and Reinforcement Learning.

Siddharth Chandak, Vivek S. Borkar

CoRR, 2021