The Randomized Midpoint Method for Log-Concave Sampling. - Ruoqi Shen; Yin Tat Lee (NeurIPS, 2019)

Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. - Yin Tat Lee; Ruoqi Shen; Kevin Tian (COLT, 2020)

Generalized Leverage Score Sampling for Neural Networks. - Jason D. Lee; Ruoqi Shen; Zhao Song 002; Mengdi Wang 001; Zheng Yu (NeurIPS, 2020)

When is Particle Filtering Efficient for POMDP Sequential Planning? - Simon S. Du; Wei Hu 014; Zhiyuan Li 005; Ruoqi Shen; Zhao Song 002; Jiajun Wu 001 (CoRR, 2020)

Composite Logconcave Sampling with a Restricted Gaussian Oracle. - Ruoqi Shen; Kevin Tian; Yin Tat Lee (CoRR, 2020)

Structured Logconcave Sampling with a Restricted Gaussian Oracle. - Yin Tat Lee; Ruoqi Shen; Kevin Tian (COLT, 2021)

Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions. - Yin Tat Lee; Ruoqi Shen; Kevin Tian (NeurIPS, 2021)

When is particle filtering efficient for planning in partially observed linear dynamical systems? - Simon S. Du; Wei Hu 014; Zhiyuan Li 005; Ruoqi Shen; Zhao Song 002; Jiajun Wu 001 (UAI, 2021)

Randomized Exploration is Near-Optimal for Tabular MDP. - Zhihan Xiong; Ruoqi Shen; Simon S. Du (CoRR, 2021)

Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev. - Sinho Chewi; Murat A. Erdogdu; Mufan (Bill) Li; Ruoqi Shen; Shunshi Zhang 001 (COLT, 2022)

Data Augmentation as Feature Manipulation. - Ruoqi Shen; Sébastien Bubeck; Suriya Gunasekar (ICML, 2022)

Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. - Yunbum Kook; Yin Tat Lee; Ruoqi Shen; Santosh S. Vempala (NeurIPS, 2022)

Near-Optimal Randomized Exploration for Tabular Markov Decision Processes. - Zhihan Xiong; Ruoqi Shen; Qiwen Cui; Maryam Fazel; Simon S. Du (NeurIPS, 2022)

On Optimal Early Stopping: Over-informative versus Under-informative Parametrization. - Ruoqi Shen; Liyao Gao; Yi-An Ma (CoRR, 2022)

Data Augmentation as Feature Manipulation: a story of desert cows and grass cows. - Ruoqi Shen; Sébastien Bubeck; Suriya Gunasekar (CoRR, 2022)

Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. - Sivakanth Gopi; Yin Tat Lee; Daogao Liu; Ruoqi Shen; Kevin Tian (COLT, 2023)

Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. - Yunbum Kook; Yin Tat Lee; Ruoqi Shen; Santosh S. Vempala (COLT, 2023)

Private Convex Optimization in General Norms. - Sivakanth Gopi; Yin Tat Lee; Daogao Liu; Ruoqi Shen; Kevin Tian (SODA, 2023)

FiLM: Fill-in Language Models for Any-Order Generation. - Tianxiao Shen; Hao Peng 009; Ruoqi Shen; Yao Fu; Zaïd Harchaoui; Yejin Choi 001 (CoRR, 2023)

Positional Description Matters for Transformers Arithmetic. - Ruoqi Shen; Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Yuanzhi Li; Yi Zhang (CoRR, 2023)

How to Fine-Tune Vision Models with SGD. - Ananya Kumar; Ruoqi Shen; Sébastien Bubeck; Suriya Gunasekar (ICLR, 2024)

Efficient Sampling Using Markov Chain Monte Carlo Methods - Ruoqi Shen (2024)