88 citations · 333 across the 26 of their papers we have counts for
12 papers · 1 filter
A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules
Xiang Li, Feng Ruan, Huiyuan Wang +2
Since ChatGPT was introduced in November 2022, embedding (nearly) unnoticeable statistical signals into text generated by large language models (LLMs), also known as watermarking,…
A Theory of Feature Learning in Kernel Models
Yunlu Chen, Yang Li, Keli Liu +1
We study feature learning in a compositional variant of kernel ridge regression in which the predictor is applied to a learnable linear transformation of the input. When the respon…
Universality of max-margin classifiers
Andrea Montanari, Feng Ruan, Basil Saeed +1
Maximum margin binary classification is one of the most fundamental algorithms in machine learning, yet the role of featurization maps and the high-dimensional asymptotics of the m…
Taming Nonconvexity in Kernel Feature Selection -- Favorable Properties of the Laplace Kernel
Feng Ruan, Keli Liu, Michael I. Jordan
Kernel-based feature selection is an important tool in nonparametric statistics. Despite many practical applications of kernel-based feature selection, there is little statistical…
The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime
Andrea Montanari, Feng Ruan, Youngtak Sohn +1
Modern machine learning classifiers often exhibit vanishing classification error on the training set. They achieve this by learning nonlinear representations of the inputs that map…
Adapting to Unknown Noise Distribution in Matrix Denoising
Andrea Montanari, Feng Ruan, Jun Yan
We consider the problem of estimating an unknown matrix , from observations where $\bold…
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information
John C. Duchi, Feng Ruan
We identify fundamental tradeoffs between statistical utility and privacy under local models of privacy in which data is kept private even from the statistician, providing instance…
A constrained risk inequality for general losses
John C. Duchi, Feng Ruan
We provide a general constrained risk inequality that applies to arbitrary non-decreasing losses, extending a result of Brown and Low [Ann. Stat. 1996]. Given two distributions $P_…
Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval
John C. Duchi, Feng Ruan
We develop procedures, based on minimization of the composition of a convex function and smooth function , for solving random collections of quadratic equal…
Asymptotic Optimality in Stochastic Optimization
John Duchi, Feng Ruan
We study local complexity measures for stochastic convex optimization problems, providing a local minimax theory analogous to that of Hájek and Le Cam for classical statistical pro…
Multiclass Classification, Information, Divergence, and Surrogate Risk
John C. Duchi, Khashayar Khosravi, Feng Ruan
We provide a unifying view of statistical information measures, multi-way Bayesian hypothesis testing, loss functions for multi-class classification problems, and multi-distributio…
Sparse Recovery via Differential Inclusions
Stanley Osher, Feng Ruan, Jiechao Xiong +2
In this paper, we recover sparse signals from their noisy linear measurements by solving nonlinear differential inclusions, which is based on the notion of inverse scale space (ISS…