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Shaofeng Zou

24 papers · 2019–2025 · 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

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+8 more ↓ πŸ—ΊοΈ Taxonomy Completionist (37) 🌍 Conference Polyglot Β· Silver πŸƒ Academic Marathon Β· Sprint πŸŒ‰ Interdisciplinary Bridge Β· Silver 🐣 Hot Topic Early Bird
πŸ”¬ Deep Specialist (10) πŸ† Grand Slam πŸ† Keyword Champion (2) πŸ“ˆ Trend Setter ⚑ Prolific Year Β· Productive πŸ”₯ Unstoppable Β· Bronze πŸ—ƒοΈ Keyword Collector (109) πŸ’Ž Century Club Β· Bronze

Venues

NeurIPS (7) ICML (5) AAAI (4) ICLR (3) AISTATS (2) UAI (2) JMLR (1)

Top co-authors

Yue Wang (9) Yi Zhou (8) Ashley Prater-Bennette (4) Zhongchang Sun (3) Ziyi Chen (3) Qi Zhang (3) Shaocong Ma (3) Yingbin Liang (3) Fei Miao (2) Simon Khan (2)

Research topics

Machine Learning (21) Optimization & Theory Β· Machine Learning (19) Reinforcement Learning (18) Methods (18) Learning Types (12) Artificial Intelligence (4) Deep Learning (3) Core AI (3) Mathematics & Optimization (2) Application Areas (2) Optimization (2) Optimization & Theory Β· Deep Learning (2) Robotics (1) Architectures (1) Capabilities (1)

Keywords

sample complexity (8) reinforcement learning (5) markov decision process (5) off-policy learning (4) linear function approximation (4) variance reduction (3) distributionally robust optimization (3) temporal difference learning (3) convergence analysis (3) robust markov decision process (3) robust reinforcement learning (3) stochastic gradient descent (2) non-convex optimization (2) stochastic approximation (2) natural actor-critic (2) bellman equation (2) neural network optimization (2) function approximation (2) policy gradient (2) model uncertainty (2)

Papers

MGDA Converges under Generalized Smoothness, Provably ICLR 2025 Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model AISTATS 2025 Revisiting Large-Scale Non-convex Distributionally Robust Optimization ICLR 2025 A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch NeurIPS 2024 Constrained Reinforcement Learning Under Model Mismatch ICML 2024 Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization AAAI 2024 Model-Free Robust Reinforcement Learning with Sample Complexity Analysis UAI 2024 Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation ICML 2024 Policy Optimization for Robust Average Reward MDPs NeurIPS 2024 Sample Complexity Characterization for Linear Contextual MDPs AISTATS 2024 Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty JMLR 2023 Model-Free Robust Average-Reward Reinforcement Learning ICML 2023 Robust Average-Reward Markov Decision Processes AAAI 2023 Policy Gradient Method For Robust Reinforcement Learning ICML 2022 Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis ICML 2022 Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity ICLR 2021 Learning Graph Neural Networks with Approximate Gradient Descent AAAI 2021 Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation NeurIPS 2021 Online Robust Reinforcement Learning with Model Uncertainty NeurIPS 2021 Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise UAI 2020 Information-Theoretic Understanding of Population Risk Improvement with Model Compression AAAI 2020 Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis NeurIPS 2020 Finite-Sample Analysis for SARSA with Linear Function Approximation NeurIPS 2019 Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples NeurIPS 2019