R. Srikant
17 papers · 2015–2025 · 7 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+6 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (28) π Conference Polyglot Β· Silver π Academic Marathon Β· Half π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
π
Keyword Champion
π
Grand Slam
π
Conference Pioneer
β‘
Prolific Year Β· Productive
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(87)
Top co-authors
Research topics
Machine Learning
(15)
Optimization & Theory
(13)
Learning Types
(9)
Reinforcement Learning
(8)
Methods
(8)
Artificial Intelligence
(5)
Core AI
(5)
Mathematics & Optimization
(3)
Optimization
(3)
Core Methods
(2)
Data Science & Analytics
(1)
Deep Learning
(1)
Applications
(1)
Learning Paradigms
(1)
Techniques
(1)
Keywords
reinforcement learning
(5)
regret bound
(4)
stochastic approximation
(3)
finite-time bound
(3)
exploration-exploitation tradeoff
(2)
linear function approximation
(2)
upper confidence bound
(2)
policy iteration
(2)
cascading bandit
(2)
finite-time analysis
(2)
mean square error
(2)
lyapunov function
(1)
function approximation
(1)
value function
(1)
asymptotic analysis
(1)
policy gradient
(1)
policy optimization
(1)
loss landscape
(1)
neural network optimization
(1)
game theory
(1)
Papers
Decentralized and Uncoordinated Learning of Stable Matchings: A Game-Theoretic Approach
AAAI 2025
Global Convergence of Policy Gradient in Average Reward MDPs
ICLR 2025
Reinforcement Learning with Segment Feedback
ICML 2025
Cascading Reinforcement Learning
ICLR 2024
Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
ICML 2024
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
ICML 2023
Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB
AISTATS 2023
Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs
L4DC 2023
On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation
AISTATS 2023
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
NeurIPS 2023
Minimax Regret for Cascading Bandits
NeurIPS 2022
The Mean-Squared Error of Double Q-Learning
NeurIPS 2020
Finite-Time Error Bounds For Linear Stochastic Approximation andTD Learning
COLT 2019
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning
NeurIPS 2019
Adding One Neuron Can Eliminate All Bad Local Minima
NeurIPS 2018
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
ICLR 2018
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
NeurIPS 2015