◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Yong Jiang

19 papers hereh-index 222.7k citations66 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author15
  • last author1

Across the 16 of 19 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CR5
  • math.OC4
  • q-fin.ST2
  • cs.CV1
same name
  • Yong Jiang — 39 papers, h 23
  • Yong Jiang — 17 papers
  • Yong Jiang — 13 papers, h 31
  • Yong Jiang — 12 papers, h 33
  • Yong Jiang — 12 papers, h 11
  • Yong Jiang — 7 papers, h 31

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182022
most citedBackdoor Attack in the Physical World

36 citations · 93 across the 8 of their papers we have counts for

collaborators
Showing math.OCShow all

4 papers · 1 filter

math.OC2021★ 11 cited

Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities

Chaobing Song, Zhengyuan Zhou, Yichao Zhou +2

The optimization problems associated with training generative adversarial neural networks can be largely reduced to certain {\em non-monotone} variational inequality problems (VIPs…

math.OC2020

Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization

Chaobing Song, Yong Jiang, Yi Ma

In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Variance Reduction via Accelerated Dual Averaging (VRADA)}. In both gene…

math.OC2020

Breaking the O(1/ε) Optimal Rate for a Class of Minimax Problems

Chaobing Song, Yong Jiang, Yi Ma

It is known that for convex optimization minw∈W​f(w), the best possible rate of first order accelerated methods is O(1/ε​). However, for th…

math.OC2019★ 3 cited

Inexact Proximal Cubic Regularized Newton Methods for Convex Optimization

Chaobing Song, Ji Liu, Yong Jiang

In this paper, we use Proximal Cubic regularized Newton Methods (PCNM) to optimize the sum of a smooth convex function and a non-smooth convex function, where we use inexact gradie…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.