Mathematical Theories of Interaction with Oracles. - Lin Yang 011 (2013)

New Time-Space Upperbounds for Directed Reachability in High-genus and H-minor-free Graphs. - Diptarka Chakraborty; Aduri Pavan; Raghunath Tewari; N. V. Vinodchandran; Lin F. Yang (FSTTCS, 2014)

Streaming Algorithms for Halo Finders. - Zaoxing Liu; Nikita Ivkin; Lin Yang 011; Mark Neyrinck; Gerard Lemson; Alexander S. Szalay; Vladimir Braverman; Tamas Budavari; Randal C. Burns; Xin Wang (e-Science, 2015)

New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory. - Vladimir Braverman; Zaoxing Liu; Tejasvam Singh; N. V. Vinodchandran; Lin F. Yang (MFCS (2), 2015)

Streaming Symmetric Norms via Measure Concentration. - Vladimir Braverman; Stephen R. Chestnut; Robert Krauthgamer; Lin F. Yang (CoRR, 2015)

Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. - Vladimir Braverman; Stephen R. Chestnut; David P. Woodruff; Lin F. Yang (PODS, 2016)

Sketches for Matrix Norms: Faster, Smaller and More General. - Vladimir Braverman; Stephen R. Chestnut; Robert Krauthgamer; Lin F. Yang (CoRR, 2016)

Clustering High Dimensional Dynamic Data Streams. - Vladimir Braverman; Gereon Frahling; Harry Lang; Christian Sohler; Lin F. Yang (ICML, 2017)

Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. - Zhehui Chen; Lin F. Yang; Chris Junchi Li; Tuo Zhao (ICML, 2017)

On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning. - Xingguo Li; Lin Yang 011; Jason Ge; Jarvis D. Haupt; Tong Zhang 001; Tuo Zhao (NIPS, 2017)

Streaming symmetric norms via measure concentration. - Jaroslaw Blasiok; Vladimir Braverman; Stephen R. Chestnut; Robert Krauthgamer; Lin F. Yang (STOC, 2017)

On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions. - Xingguo Li; Lin F. Yang; Jason Ge; Jarvis D. Haupt; Tong Zhang 001; Tuo Zhao (CoRR, 2017)

Dynamic Factorization and Partition of Complex Networks. - Lin F. Yang; Vladimir Braverman; Tuo Zhao; Mengdi Wang 001 (CoRR, 2017)

Misspecified Nonconvex Statistical Optimization for Phase Retrieval. - Zhuoran Yang; Lin F. Yang; Ethan X. Fang; Tuo Zhao; Zhaoran Wang 001; Matey Neykov (CoRR, 2017)

Approximate Convex Hull of Data Streams. - Avrim Blum; Vladimir Braverman; Ananya Kumar; Harry Lang; Lin F. Yang (ICALP, 2018)

Revisiting Frequency Moment Estimation in Random Order Streams. - Vladimir Braverman; Emanuele Viola; David P. Woodruff; Lin F. Yang (ICALP, 2018)

Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order. - Vladimir Braverman; Stephen R. Chestnut; Robert Krauthgamer; Yi Li 002; David P. Woodruff; Lin F. Yang (ICML, 2018)

Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. - Minshuo Chen; Lin Yang 011; Mengdi Wang 001; Tuo Zhao (NeurIPS, 2018)

Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. - Aaron Sidford; Mengdi Wang 001; Xian Wu 009; Lin Yang 011; Yinyu Ye 001 (NeurIPS, 2018)

The Physical Systems Behind Optimization Algorithms. - Lin F. Yang; Raman Arora; Vladimir Braverman; Tuo Zhao (NeurIPS, 2018)

Scalable streaming tools for analyzing N-body simulations: Finding halos and investigating excursion sets in one pass. - Nikita Ivkin; Zaoxing Liu; Lin F. Yang; S. S. Kumar 004; Gerard Lemson; Mark Neyrinck; Alexander S. Szalay; Vladimir Braverman; Tamas Budavari (Astron. Comput., 2018)

Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to k-Clustering. - Zhao Song 002; Lin F. Yang; Peilin Zhong (CoRR, 2018)

On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization. - Zhehui Chen; Xingguo Li; Lin F. Yang; Jarvis D. Haupt; Tuo Zhao (CoRR, 2018)

On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition. - Zhehui Chen; Xingguo Li; Lin Yang 011; Jarvis D. Haupt; Tuo Zhao (AISTATS, 2019)

Towards a Theoretical Understanding of Hashing-Based Neural Nets. - Yibo Lin; Zhao Song 002; Lin F. Yang (AISTATS, 2019)

Learning to Control in Metric Space with Optimal Regret. - Chengzhuo Ni; Lin F. Yang; Mengdi Wang 001 (Allerton, 2019)

The One-Way Communication Complexity of Dynamic Time Warping Distance. - Vladimir Braverman; Moses Charikar; William Kuszmaul; David P. Woodruff; Lin F. Yang (SoCG, 2019)

Sample-Optimal Parametric Q-Learning Using Linearly Additive Features. - Lin Yang 011; Mengdi Wang 001 (ICML, 2019)

Efficient Symmetric Norm Regression via Linear Sketching. - Zhao Song 002; Ruosong Wang; Lin F. Yang; Hongyang Zhang 001; Peilin Zhong (NeurIPS, 2019)

Online Factorization and Partition of Complex Networks by Random Walk. - Lin F. Yang; Zheng Yu; Vladimir Braverman; Tuo Zhao; Mengdi Wang 001 (UAI, 2019)

Sample-Optimal Parametric Q-Learning with Linear Transition Models. - Lin F. Yang; Mengdi Wang 001 (CoRR, 2019)

Reinforcement Leaning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. - Lin F. Yang; Mengdi Wang 001 (CoRR, 2019)

Feature-Based Q-Learning for Two-Player Stochastic Games. - Zeyu Jia; Lin F. Yang; Mengdi Wang 001 (CoRR, 2019)

On the Optimality of Sparse Model-Based Planning for Markov Decision Processes. - Alekh Agarwal; Sham M. Kakade; Lin F. Yang (CoRR, 2019)

Continuous Control with Contexts, Provably. - Simon S. Du; Ruosong Wang; Mengdi Wang 001; Lin F. Yang (CoRR, 2019)

Does Knowledge Transfer Always Help to Learn a Better Policy? - Fei Feng; Wotao Yin; Lin F. Yang (CoRR, 2019)

Misspecified nonconvex statistical optimization for sparse phase retrieval. - Zhuoran Yang; Lin F. Yang; Ethan X. Fang; Tuo Zhao; Zhaoran Wang 001; Matey Neykov (Math. Program., 2019)

Sketching Transformed Matrices with Applications to Natural Language Processing. - Yingyu Liang; Zhao Song 002; Mengdi Wang 001; Lin Yang 011; Xin Yang 017 (AISTATS, 2020)

Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. - Aaron Sidford; Mengdi Wang 001; Lin Yang 011; Yinyu Ye 001 (AISTATS, 2020)

Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal. - Alekh Agarwal; Sham M. Kakade; Lin F. Yang (COLT, 2020)

Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? - Simon S. Du; Sham M. Kakade; Ruosong Wang; Lin F. Yang (ICLR, 2020)

Model-Based Reinforcement Learning with Value-Targeted Regression. - Alex Ayoub; Zeyu Jia; Csaba Szepesvári; Mengdi Wang 001; Lin Yang 011 (ICML, 2020)

Nearly Linear Row Sampling Algorithm for Quantile Regression. - Yi Li 002; Ruosong Wang; Lin Yang 011; Hanrui Zhang 001 (ICML, 2020)

Obtaining Adjustable Regularization for Free via Iterate Averaging. - Jingfeng Wu; Vladimir Braverman; Lin Yang 011 (ICML, 2020)

Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. - Lin Yang 011; Mengdi Wang 001 (ICML, 2020)

Model-Based Reinforcement Learning with Value-Targeted Regression. - Zeyu Jia; Lin Yang 011; Csaba Szepesvári; Mengdi Wang 001 (L4DC, 2020)

Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? - Qiwen Cui; Lin F. Yang (NeurIPS, 2020)

Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. - Fei Feng; Ruosong Wang; Wotao Yin; Simon S. Du; Lin F. Yang (NeurIPS, 2020)

Toward the Fundamental Limits of Imitation Learning. - Nived Rajaraman; Lin F. Yang; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2020)

Is Long Horizon RL More Difficult Than Short Horizon RL? - Ruosong Wang; Simon S. Du; Lin F. Yang; Sham M. Kakade (NeurIPS, 2020)

On Reward-Free Reinforcement Learning with Linear Function Approximation. - Ruosong Wang; Simon S. Du; Lin F. Yang; Ruslan Salakhutdinov (NeurIPS, 2020)

Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension. - Ruosong Wang; Ruslan Salakhutdinov; Lin F. Yang (NeurIPS, 2020)

Planning with General Objective Functions: Going Beyond Total Rewards. - Ruosong Wang; Peilin Zhong; Simon S. Du; Ruslan Salakhutdinov; Lin F. Yang (NeurIPS, 2020)

Preference-based Reinforcement Learning with Finite-Time Guarantees. - Yichong Xu; Ruosong Wang; Lin F. Yang; Aarti Singh; Artur Dubrawski (NeurIPS, 2020)

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. - Kaiqing Zhang; Sham M. Kakade; Tamer Basar; Lin F. Yang (NeurIPS, 2020)

Deep Reinforcement Learning with Linear Quadratic Regulator Regions. - Gabriel I. Fernandez; Colin Togashi; Dennis W. Hong; Lin F. Yang (CoRR, 2020)

Provably Efficient Exploration for RL with Unsupervised Learning. - Fei Feng; Ruosong Wang; Wotao Yin; Simon S. Du; Lin F. Yang (CoRR, 2020)

Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? - Ruosong Wang; Simon S. Du; Lin F. Yang; Sham M. Kakade (CoRR, 2020)

Provably Efficient Reinforcement Learning with General Value Function Approximation. - Ruosong Wang; Ruslan Salakhutdinov; Lin F. Yang (CoRR, 2020)

Random Walk Bandits. - Tianyu Wang; Lin F. Yang; Zizhuo Wang 001 (CoRR, 2020)

Episodic Linear Quadratic Regulators with Low-rank Transitions. - Tianyu Wang; Lin F. Yang (CoRR, 2020)

The one-way communication complexity of dynamic time warping distance. - Vladimir Braverman; Moses Charikar; William Kuszmaul; Lin F. Yang (J. Comput. Geom., 2020)

Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation. - Junhong Shen; Lin F. Yang (AAAI, 2021)

Q-learning with Logarithmic Regret. - Kunhe Yang; Lin F. Yang; Simon S. Du (AISTATS, 2021)

Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning. - Yuanzhi Li; Ruosong Wang; Lin F. Yang (FOCS, 2021)

Safe Reinforcement Learning with Linear Function Approximation. - Sanae Amani; Christos Thrampoulidis; Lin Yang 011 (ICML, 2021)

Provably Correct Optimization and Exploration with Non-linear Policies. - Fei Feng; Wotao Yin; Alekh Agarwal; Lin Yang 011 (ICML, 2021)

Randomized Exploration in Reinforcement Learning with General Value Function Approximation. - Haque Ishfaq; Qiwen Cui; Viet Nguyen; Alex Ayoub; Zhuoran Yang; Zhaoran Wang 001; Doina Precup; Lin Yang 011 (ICML, 2021)

Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. - Jialin Dong; Da Zheng 004; Lin F. Yang; George Karypis (KDD, 2021)

On the Value of Interaction and Function Approximation in Imitation Learning. - Nived Rajaraman; Yanjun Han; Lin Yang 011; Jingbo Liu; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2021)

Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. - Jingfeng Wu; Vladimir Braverman; Lin Yang 011 (NeurIPS, 2021)

Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs. - Han Zhong 001; Jiayi Huang; Lin Yang 011; Liwei Wang 001 (NeurIPS, 2021)

Minimax sample complexity for turn-based stochastic game. - Qiwen Cui; Lin F. Yang (UAI, 2021)

A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost. - Minbo Gao; Tianle Xie; Simon S. Du; Lin F. Yang (CoRR, 2021)

Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally. - Nived Rajaraman; Yanjun Han; Lin F. Yang; Kannan Ramchandran; Jiantao Jiao (CoRR, 2021)

Online Sub-Sampling for Reinforcement Learning with General Function Approximation. - Dingwen Kong; Ruslan Salakhutdinov; Ruosong Wang; Lin F. Yang (CoRR, 2021)

Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration. - Weichao Mao; Tamer Basar; Lin F. Yang; Kaiqing Zhang (CoRR, 2021)

Solving Multi-Arm Bandit Using a Few Bits of Communication. - Osama A. Hanna; Lin Yang 011; Christina Fragouli (AISTATS, 2022)

Gap-Dependent Unsupervised Exploration for Reinforcement Learning. - Jingfeng Wu; Vladimir Braverman; Lin Yang 011 (AISTATS, 2022)

Doubly Pessimistic Algorithms for Strictly Safe Off-Policy Optimization. - Sanae Amani; Lin F. Yang (CISS, 2022)

Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver. - Xiaoyu Chen 008; Jiachen Hu; Lin Yang 011; Liwei Wang 001 (ICLR, 2022)

On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning. - Weichao Mao; Lin Yang 011; Kaiqing Zhang; Tamer Basar (ICML, 2022)

Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context. - Osama A. Hanna; Lin Yang 011; Christina Fragouli (NeurIPS, 2022)

Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning. - Dingwen Kong; Lin Yang 011 (NeurIPS, 2022)

Near-Optimal Sample Complexity Bounds for Constrained MDPs. - Sharan Vaswani; Lin Yang 011; Csaba Szepesvári (NeurIPS, 2022)

Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation. - Sanae Amani; Lin F. Yang; Ching-An Cheng (CoRR, 2022)

Learning in Distributed Contextual Linear Bandits Without Sharing the Context. - Osama A. Hanna; Lin F. Yang; Christina Fragouli (CoRR, 2022)

From Local to Global: Spectral-Inspired Graph Neural Networks. - Ningyuan Huang; Soledad Villar; Carey E. Priebe; Da Zheng 004; Chengyue Huang; Lin Yang 011; Vladimir Braverman (CoRR, 2022)

Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP. - Jinghan Wang; Mengdi Wang 001; Lin F. Yang (CoRR, 2022)

Universal Streaming of Subset Norms. - Vladimir Braverman; Robert Krauthgamer; Lin F. Yang (Theory Comput., 2022)

Compression for Multi-Arm Bandits. - Osama A. Hanna; Lin F. Yang; Christina Fragouli (IEEE J. Sel. Areas Inf. Theory, 2022)

MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models. - Masoud Monajatipoor; Liunian Harold Li; Mozhdeh Rouhsedaghat; Lin Yang 011; Kai-Wei Chang (ACL (2), 2023)

Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms. - Osama A. Hanna; Lin Yang 011; Christina Fragouli (COLT, 2023)

Provably Efficient Lifelong Reinforcement Learning with Linear Representation. - Sanae Amani; Lin Yang 011; Ching-An Cheng (ICLR, 2023)

Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. - Sanae Amani; Tor Lattimore; András György 001; Lin Yang 011 (ICML, 2023)

Does Sparsity Help in Learning Misspecified Linear Bandits? - Jialin Dong; Lin Yang 011 (ICML, 2023)

Multi-Arm Bandits over Action Erasure Channels. - Osama A. Hanna; Merve Karakas; Lin F. Yang; Christina Fragouli (ISIT, 2023)

Efficient Batched Algorithm for Contextual Linear Bandits with Large Action Space via Soft Elimination. - Osama A. Hanna; Lin Yang 011; Christina Fragouli (NeurIPS, 2023)

Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds. - Jiayi Huang; Han Zhong 001; Liwei Wang 001; Lin Yang 011 (NeurIPS, 2023)

Replicability in Reinforcement Learning. - Amin Karbasi; Grigoris Velegkas; Lin Yang 011; Felix Zhou 002 (NeurIPS, 2023)

Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. - Sanae Amani; Khushbu Pahwa; Vladimir Braverman; Lin F. Yang (CoRR, 2023)

Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning. - Haochen Zhang; Xi Chen; Lin F. Yang (CoRR, 2023)

Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels. - Osama A. Hanna; Merve Karakas; Lin Yang 011; Christina Fragouli (AISTATS, 2024)

Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation. - Jiayi Huang; Han Zhong 001; Liwei Wang 001; Lin Yang 011 (AISTATS, 2024)

Wearable Body Sensors Integrated into a Virtual Reality Environment - A Modality for Automating the Rehabilitation of the Motor Control System. - Seungmin Jung; Mianzhi Zhou; Ji Ma 006; Ryan Yang; Steven C. Cramer; Bruce H. Dobkin; Lin F. Yang; Jacob Rosen 001 (EMBC, 2024)

Delayed MDPs with Feature Mapping. - Jialin Dong; Jiayi Wang; Lin F. Yang (IJCNN, 2024)

Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning. - Junyan Liu; Yunfan Li; Ruosong Wang; Lin Yang 011 (NeurIPS, 2024)

Confident Natural Policy Gradient for Local Planning in qπ-realizable Constrained MDPs. - Tian Tian; Lin Yang 011; Csaba Szepesvári (NeurIPS, 2024)

Provably Correct SGD-Based Exploration for Generalized Stochastic Bandit Problem. - Jialin Dong; Jiayi Wang; Lin F. Yang (SmartNets, 2024)

Don't Forget to Connect! Improving RAG with Graph-based Reranking. - Jialin Dong; Bahare Fatemi; Bryan Perozzi; Lin F. Yang; Anton Tsitsulin (CoRR, 2024)

Learning for Bandits under Action Erasures. - Osama A. Hanna; Merve Karakas; Lin F. Yang; Christina Fragouli (CoRR, 2024)

Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning. - Yiran Wang; Chenshu Liu; Yunfan Li; Sanae Amani; Bolei Zhou; Lin F. Yang (CoRR, 2024)

A collective AI via lifelong learning and sharing at the edge. - Andrea Soltoggio; Eseoghene Ben-Iwhiwhu; Vladimir Braverman; Eric Eaton; Benjamin Epstein; Yunhao Ge; Lucy Halperin; Jonathan P. How; Laurent Itti; Michael A. Jacobs; Pavan Kantharaju; Long Le; et al. (Nat. Mac. Intell., 2024)

Modeling Bellman-error with logistic distribution with applications in reinforcement learning. - Outongyi Lv; Bingxin Zhou; Lin F. Yang (Neural Networks, 2024)

Federated Learning With Massive Random Access. - Shuhao Xia; Yuanming Shi; Yong Zhou 006; Youlong Wu; Lin F. Yang; Khaled B. Letaief (IEEE Trans. Wirel. Commun., 2024)

Regret-Optimal List Replicable Bandit Learning: Matching Upper and Lower Bounds. - Michael Chen; Aduri Pavan; N. V. Vinodchandran; Ruosong Wang; Lin Yang 011 (ICLR, 2025)

Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error. - Ally Yalei Du; Lin Yang 011; Ruosong Wang (ICLR, 2025)

Does Feedback Help in Bandits with Arm Erasures? - Merve Karakas; Osama A. Hanna; Lin F. Yang; Christina Fragouli (ISIT, 2025)

I3S: Importance Sampling Subspace Selection for Low-Rank Optimization in LLM Pretraining. - Haochen Zhang; Junze Yin; Guanchu Wang; Zirui Liu 001; Tianyi Zhang 011; Anshumali Shrivastava; Lin Yang 011; Vladimir Braverman (CoRR, 2025)

NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models. - Lawrence Liu; Inesh Chakrabarti; Yixiao Li; Mengdi Wang 001; Tuo Zhao; Lin F. Yang (CoRR, 2025)

On the optimal regret of collaborative personalized linear bandits. - Bruce Huang; Ruida Zhou; Lin F. Yang; Suhas N. Diggavi (CoRR, 2025)

Sample Complexity Bounds for Linear Constrained MDPs with a Generative Model. - Xingtu Liu 001; Lin F. Yang; Sharan Vaswani (CoRR, 2025)

Gemini 2.5 Pro Capable of Winning Gold at IMO 2025. - Yichen Huang; Lin F. Yang (CoRR, 2025)

Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs. - Yukuan Wei; Xudong Li; Lin F. Yang (CoRR, 2025)

ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization. - Lawrence Liu; Alexander Liu; Mengdi Wang 001; Tuo Zhao; Lin F. Yang (CoRR, 2025)