← Authors

Lei Ying

20 papers · 2019–2025 · 7 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (9) πŸ—ΊοΈ Taxonomy Completionist (27) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (6) 🧭 Keyword Pioneer πŸ† Keyword Champion (3) πŸ† Grand Slam πŸ”₯ Unstoppable (7) πŸ’Ž Century Club (20) ⚑ Prolific Year (6) πŸ—ƒοΈ Keyword Collector (93)

Conferences

NIPS (7) AAAI (4) AISTATS (3) ICML (3) COLT (1) ICLR (1) JMLR (1)

Top co-authors

Xin Liu (8) Honghao Wei (6) R. Srikant (4) Zixian Yang (3) Qining Zhang (3) Hanghang Tong (2) Harsh Gupta (2) Xian Yu (1) Yuheng Zhang (1) Ness Shroff (1)

Keywords

regret bound (6) constraint violation (4) constrained markov decision process (3) reinforcement learning (3) constrained mdp (3) stochastic approximation (3) upper confidence bound (2) model-free algorithm (2) optimal stopping (2) model-free reinforcement learning (2) online convex optimization (2) sublinear regret (2) policy gradient (2) multi-armed bandit (2) linear function approximation (2) online algorithm (2) regret minimization (1) constrained optimization (1) computational complexity (1) sample complexity (1)

Papers

Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference ICLR 2025
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment JMLR 2024
Safe Reinforcement Learning with Instantaneous Constraints: The Role of Aggressive Exploration AAAI 2024
Deep Reinforcement Learning for Early Diagnosis of Lung Cancer AAAI 2024
Graph Mixup on Approximate Gromov–Wasserstein Geodesics ICML 2024
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks NIPS 2023
Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms NIPS 2023
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures ICML 2023
Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB AISTATS 2023
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs AISTATS 2023
Online Nonstochastic Control with Adversarial and Static Constraints ICML 2023
Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning AAAI 2022
Will Bilevel Optimizers Benefit from Loops NIPS 2022
Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond NIPS 2022
Triple-Q: A Model-Free Algorithm for Constrained Reinforcement Learning with Sublinear Regret and Zero Constraint Violation AISTATS 2022
A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes AAAI 2022
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints NIPS 2021
The Mean-Squared Error of Double Q-Learning NIPS 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 NIPS 2019