← Authors

Zhuoran Yang

128 papers · 2015–2025 · 10 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (10) 🐝 Cross-Pollinator (13) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (10) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (22) 🏠 Conference Loyalist (41) 🀝 Dynamic Duo (96) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (33) 🧬 Topic Evolution πŸ† Keyword Champion (15) πŸ”₯ Unstoppable (11) ⚑ Prolific Year (14) ❓ The Questioner (7) πŸ“ˆ Trend Setter πŸ’Ž Century Club (128) πŸ—ƒοΈ Keyword Collector (92) πŸš€ Conference Pioneer

Conferences

ICML (47) NIPS (41) ICLR (17) JMLR (8) AISTATS (7) L4DC (3) COLT (2) ACL (1) CORL (1) ICCV (1)

Top co-authors

Zhaoran Wang (96) Qi Cai (11) Yufeng Zhang (9) Lingxiao Wang (9) Han Liu (8) Shuang Qiu (8) Siyu Chen (7) Han Zhong (7) Mladen Kolar (7) Zuyue Fu (7)

Research topics

Reinforcement Learning (1) Applications (1)

Keywords

regret bound (19) reinforcement learning (18) function approximation (15) neural network (12) offline reinforcement learning (10) policy optimization (9) multi-agent system (9) markov decision process (9) nash equilibrium (8) linear function approximation (8) multi-agent reinforcement learning (8) representation learning (8) sample complexity (7) zero-sum game (6) value iteration (6) bilevel optimization (5) causal inference (4) partially observable markov decision process (4) exponential family (4) policy gradient (4)

Papers

BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms ICML 2025
In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention ICML 2025
In-Context Reinforcement Learning From Suboptimal Historical Data ICML 2025
Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model ICLR 2025
An Instrumental Value for Data Production and its Application to Data Pricing ICML 2025
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF JMLR 2025
Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization ICML 2025
InstaDrive: Instance-Aware Driving World Models for Realistic and Consistent Video Generation ICCV 2025
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization AISTATS 2025
Learning Task Representations from In-Context Learning ACL 2025
Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation CORL 2025
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability ICML 2025
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems ICLR 2024
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games NIPS 2024
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers NIPS 2024
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach JMLR 2024
Learning Regularized Graphon Mean-Field Games with Unknown Graphons JMLR 2024
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning JMLR 2024
How Does Goal Relabeling Improve Sample Efficiency? ICML 2024
Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning ICML 2024
A General Framework for Sequential Decision-Making under Adaptivity Constraints ICML 2024
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF ICML 2024
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems ICML 2024
Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling ICML 2024
Sample-Efficient Multi-Agent RL: An Optimization Perspective ICLR 2024
Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation ICLR 2024
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning L4DC 2023
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration NIPS 2023
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation NIPS 2023
Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games NIPS 2023
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning NIPS 2023
Learning Regularized Monotone Graphon Mean-Field Games NIPS 2023
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning AISTATS 2023
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games ICLR 2023
Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics ICLR 2023
Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency ICLR 2023
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes ICLR 2023
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization ICLR 2023
Can We Find Nash Equilibria at a Linear Rate in Markov Games? ICLR 2023
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model ICML 2023
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP ICML 2023
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments ICML 2023
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning ICML 2023
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? JMLR 2023
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning JMLR 2023
Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation ICML 2022
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency ICML 2022
Adaptive Model Design for Markov Decision Process ICML 2022
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation ICML 2022
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes ICML 2022
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy ICML 2022
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning ICML 2022
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning ICML 2022
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets ICML 2022
Reinforcement Learning with Logarithmic Regret and Policy Switches NIPS 2022
Gap-Dependent Bounds for Two-Player Markov Games AISTATS 2022
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL NIPS 2022
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence NIPS 2022
Exponential Family Model-Based Reinforcement Learning via Score Matching NIPS 2022
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets NIPS 2022
A Unifying Framework of Off-Policy General Value Function Evaluation NIPS 2022
Towards General Function Approximation in Zero-Sum Markov Games ICLR 2022
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory ICLR 2022
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning ICLR 2022
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration NIPS 2021
Is Pessimism Provably Efficient for Offline RL? ICML 2021
Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport ICML 2021
BooVI: Provably Efficient Bootstrapped Value Iteration NIPS 2021
Sample Elicitation AISTATS 2021
Reinforcement Learning for Cost-Aware Markov Decision Processes ICML 2021
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time ICML 2021
Learning While Playing in Mean-Field Games: Convergence and Optimality ICML 2021
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality ICML 2021
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic NIPS 2021
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions ICML 2021
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL NIPS 2021
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach ICML 2021
Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games ICML 2021
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game ICML 2021
Randomized Exploration in Reinforcement Learning with General Value Function Approximation ICML 2021
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization AISTATS 2021
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy ICLR 2021
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum NIPS 2021
Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case AISTATS 2021
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning NIPS 2021
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data NIPS 2021
Provably Sample Efficient Reinforcement Learning in Competitive Linear Quadratic Systems L4DC 2021
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework NIPS 2020
Provably efficient reinforcement learning with linear function approximation COLT 2020
Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium COLT 2020
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence ICLR 2020
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games ICLR 2020
On Computation and Generalization of Generative Adversarial Imitation Learning ICLR 2020
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret NIPS 2020
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory NIPS 2020
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss NIPS 2020
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations NIPS 2020
Provably Efficient Exploration in Policy Optimization ICML 2020
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees ICML 2020
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis ICML 2020
On the Global Optimality of Model-Agnostic Meta-Learning ICML 2020
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning ICML 2020
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate ICML 2020
A Theoretical Analysis of Deep Q-Learning L4DC 2020
Provably Efficient Neural GTD for Off-Policy Learning NIPS 2020
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach NIPS 2020
Dynamic Regret of Policy Optimization in Non-Stationary Environments NIPS 2020
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data ICML 2019
Neural Temporal-Difference Learning Converges to Global Optima NIPS 2019
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost NIPS 2019
Variance Reduced Policy Evaluation with Smooth Function Approximation NIPS 2019
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games NIPS 2019
Convergent Policy Optimization for Safe Reinforcement Learning NIPS 2019
Statistical-Computational Tradeoff in Single Index Models NIPS 2019
High-dimensional Varying Index Coefficient Models via Stein's Identity JMLR 2019
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy NIPS 2019
Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding AISTATS 2018
Provable Gaussian Embedding with One Observation NIPS 2018
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents ICML 2018
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference ICML 2018
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization NIPS 2018
On Semiparametric Exponential Family Graphical Models JMLR 2018
Contrastive Learning from Pairwise Measurements NIPS 2018
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation ICML 2017
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma NIPS 2017
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning NIPS 2016
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity ICML 2016
Human Memory Search as Initial-Visit Emitting Random Walk NIPS 2015