25 citations · 98 across the 17 of their papers we have counts for
10 papers · 1 filter
Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu +2
We propose a new framework for differentially private optimization of convex functions which are Lipschitz in an arbitrary norm . Our algorithms are based on a regulariz…
Data Augmentation as Feature Manipulation
Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar
Data augmentation is a cornerstone of the machine learning pipeline, yet its theoretical underpinnings remain unclear. Is it merely a way to artificially augment the data set size?…
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization
Ruoqi Shen, Liyao Gao, Yi-An Ma
Early stopping is a simple and widely used method to prevent over-training neural networks. We develop theoretical results to reveal the relationship between the optimal early stop…
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
Yunbum Kook, Yin Tat Lee, Ruoqi Shen +1
We demonstrate for the first time that ill-conditioned, non-smooth, constrained distributions in very high dimension, upwards of 100,000, can be sampled efficiently $\textit{in pra…
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
Zhihan Xiong, Ruoqi Shen, Qiwen Cui +2
We study algorithms using randomized value functions for exploration in reinforcement learning. This type of algorithms enjoys appealing empirical performance. We show that when we…
Generalized Leverage Score Sampling for Neural Networks
Jason D. Lee, Ruoqi Shen, Zhao Song +2
Leverage score sampling is a powerful technique that originates from theoretical computer science, which can be used to speed up a large number of fundamental questions, e.g. linea…
Composite Logconcave Sampling with a Restricted Gaussian Oracle
Ruoqi Shen, Kevin Tian, Yin Tat Lee
We consider sampling from composite densities on of the form for well-conditioned and convex (but possibly non-smooth) ,…
When is Particle Filtering Efficient for Planning in Partially Observed Linear Dynamical Systems?
Simon S. Du, Wei Hu, Zhiyuan Li +3
Particle filtering is a popular method for inferring latent states in stochastic dynamical systems, whose theoretical properties have been well studied in machine learning and stat…
Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo
Yin Tat Lee, Ruoqi Shen, Kevin Tian
We show that the gradient norm for , where is strongly convex and smooth, concentrates tightly around its mean. This removes a barrier in…
The Randomized Midpoint Method for Log-Concave Sampling
Ruoqi Shen, Yin Tat Lee
Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form $p^{*}\pr…