Scholar
Siddharth Chandak
Google Scholar ID: czi8jdYAAAAJ
Stanford University
Multi-Agent Learning
Reinforcement Learning
Game Theory
Stochastic Approximation
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Citations & Impact
All-time
Citations
98
H-index
5
i10-index
3
Publications
16
Co-authors
10
list available
Publications
13 items
Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach
2026
Cited
0
Policy Gradient Methods for Non-Markovian Reinforcement Learning
2026
Cited
0
Last-Iterate Guarantees for Learning in Co-coercive Games
2026
Cited
0
Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
2026
Cited
0
High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
2026
Cited
0
Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains
2026
Cited
0
Choose Your Battles: Distributed Learning Over Multiple Tug of War Games
2025
Cited
0
$O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation
2025
Cited
0
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Co-authors
7 total
Vivek Borkar
Indian Institute of Technology Bombay
Ilai Bistritz
Tel Aviv University
Shaan Ul haque
Georgia Institute of Technology
Petar Popovski
Professor, Connectivity, Aalborg University, Denmark
Federico Chiariotti
Assistant Professor, University of Padova
Deniz Gunduz
Professor of Information Processing, Imperial College London
Pratik Shah
Student, Georgia Institute of Technology
