activity
20182023
most citedOn Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration

21 citations · 22 across the 3 of their papers we have counts for

collaborators
Showing 2018Show all

5 papers · 1 filter

cs.LG2018

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack

Haishan Ye, Zhichao Huang, Cong Fang +2

Zeroth-order optimization is an important research topic in machine learning. In recent years, it has become a key tool in black-box adversarial attack to neural network based imag…

math.PR2018

A note on concentration inequality for vector-valued martingales with weak exponential-type tails

Chris Junchi Li

We present novel martingale concentration inequalities for martingale differences with finite Orlicz- norms. Such martingale differences with weak exponential-type tails scatt…

stat.ML2018

Diffusion Approximations for Online Principal Component Estimation and Global Convergence

Chris Junchi Li, Mengdi Wang, Han Liu +1

In this paper, we propose to adopt the diffusion approximation tools to study the dynamics of Oja's iteration which is an online stochastic gradient descent method for the principa…

stat.ML2018

Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

Chris Junchi Li, Zhaoran Wang, Han Liu

Solving statistical learning problems often involves nonconvex optimization. Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, esp…

math.OC2018

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator

Cong Fang, Chris Junchi Li, Zhouchen Lin +1

In this paper, we propose a new technique named \textit{Stochastic Path-Integrated Differential EstimatoR} (SPIDER), which can be used to track many deterministic quantities of int…