Publications

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)

Preprints

• 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