α-β stands for alphabetical author order. * stands for equal contribution.
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. (α-β) 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. (α-β) Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala COLT 2023
Private Convex Optimization in General Norms. (α-β) Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian SODA 2023
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. (α-β) Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala NeurIPS 2022
Randomized Exploration is Near-Optimal for Tabular MDP. Zhihan Xiong*, Ruoqi Shen*, Qiwen Cui*, Maryam Fazel, Simon S. Du NeurIPS 2022
Data Augmentation as Feature Manipulation. Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar ICML 2022
Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev. (α-β) Sinho Chewi, Murat A. Erdogdu, Mufan Bill Li, Ruoqi Shen, Matthew Zhang COLT 2022
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions. (α-β) Yin Tat Lee, Ruoqi Shen, Kevin Tian NeurIPS 2021 (oral presentation)
Structured Logconcave Sampling with a Restricted Gaussian Oracle. (α-β) Yin Tat Lee, Ruoqi Shen, Kevin Tian COLT 2021
When is Particle Filtering Efficient for POMDP Sequential Planning? (α-β) Simon S. Du, Wei Hu, Zhiyuan Li, Ruoqi Shen, Zhao Song, Jiajun Wu UAI 2021
Generalized Leverage Score Sampling for Neural Networks. (α-β) Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu NeurIPS 2020
Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. (α-β) Yin Tat Lee, Ruoqi Shen, Kevin Tian COLT 2020
The Randomized Midpoint Method for Log-Concave Sampling. Ruoqi Shen, Yin Tat Lee NeurIPS 2019 (spotlight presentation)
Positional Description Matters for Transformers Arithmetic. Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Yuanzhi Li, Yi Zhang
FiLM: Fill-in Language Models for Any-Order Generation Tianxiao Shen, Hao Peng, Ruoqi Shen, Yao Fu, Zaid Harchaoui, Yejin Choi
How to Fine-Tune Vision Models with SGD. Ananya Kumar, Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization. Ruoqi Shen, Liyao Gao, Yi-An Ma