3 citations · 11 across the 16 of their papers we have counts for
16 papers
Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
Lesi Chen, Boyuan Yao, Luo Luo
This paper considers stochastic first-order algorithms for minimax optimization under Polyak--Łojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem…
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles
Lesi Chen, Yaohua Ma, Jingzhao Zhang
In this work, we consider bilevel optimization when the lower-level problem is strongly convex. Recent works show that with a Hessian-vector product (HVP) oracle, one can provably…
Communication Efficient Distributed Newton Method with Fast Convergence Rates
Chengchang Liu, Lesi Chen, Luo Luo +1
We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires communication c…
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
Lesi Chen, Jing Xu, Luo Luo
We consider the optimization problem of the form , where the component is -mean-squared Lipschitz but…
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis
Lesi Chen, Jing Xu, Jingzhao Zhang
Bilevel optimization reveals the inner structure of otherwise oblique optimization problems, such as hyperparameter tuning, neural architecture search, and meta-learning. A common…
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
Lesi Chen, Haishan Ye, Luo Luo
This paper studies the stochastic nonconvex-strongly-concave minimax optimization over a multi-agent network. We propose an efficient algorithm, called Decentralized Recursive grad…
Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization
Lesi Chen, Luo Luo
We study the problem of finding a near-stationary point for smooth minimax optimization. The recently proposed extra anchored gradient (EAG) methods achieve the optimal convergence…
Estimations of the Local Conditional Tail Average Treatment Effect
Le-Yu Chen, Yu-Min Yen
The conditional tail average treatment effect (CTATE) is defined as a difference between the conditional tail expectations of potential outcomes, which can capture heterogeneity an…
Sparse Quantile Regression
Le-Yu Chen, Sokbae Lee
We consider both -penalized and -constrained quantile regression estimators. For the -penalized estimator, we derive an exponential inequality on t…
Robust Ranking of Happiness Outcomes: A Median Regression Perspective
Le-Yu Chen, Ekaterina Oparina, Nattavudh Powdthavee +1
Ordered probit and logit models have been frequently used to estimate the mean ranking of happiness outcomes (and other ordinal data) across groups. However, it has been recently h…
High Dimensional Classification through -Penalized Empirical Risk Minimization
Le-Yu Chen, Sokbae Lee
We consider a high dimensional binary classification problem and construct a classification procedure by minimizing the empirical misclassification risk with a penalty on the numbe…
Exact computation of GMM estimators for instrumental variable quantile regression models
Le-Yu Chen, Sokbae Lee
We show that the generalized method of moments (GMM) estimation problem in instrumental variable quantile regression (IVQR) models can be equivalently formulated as a mixed integer…
Best Subset Binary Prediction
Le-Yu Chen, Sokbae Lee
We consider a variable selection problem for the prediction of binary outcomes. We study the best subset selection procedure by which the covariates are chosen by maximizing Manski…
Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models
Le-Yu Chen, Sokbae Lee
This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterize…
Maximum Score Estimation of Preference Parameters for a Binary Choice Model under Uncertainty
Le-Yu Chen, Sokbae Lee, Myung Jae Sung
This paper develops maximum score estimation of preference parameters in the binary choice model under uncertainty in which the decision rule is affected by conditional expectation…
Testing Multiple Inequality Hypotheses : A Smoothed Indicator Approach
Le-Yu Chen, Jerzy Szroeter
This paper proposes a class of origin-smooth approximators of indicators underlying the sum-of-negative-part statistic for testing multiple inequalities. The need for simulation or…