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Yang Yuan

16 papers hereh-index 204.5k citations26 works total

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

author position
  • middle author4
  • last author12

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

fields
  • cs.LG9
  • cs.GT3
  • cs.CG1
  • cs.DS1
  • cs.NI1
  • math.OC1
same name
  • Yang Yuan — 12 papers, h 7
  • Yang Yuan — 8 papers, h 5
  • Yang Yuan — 7 papers, h 7
  • Yang Yuan — 3 papers, h 3
  • Yang Yuan — 3 papers, h 1
  • Yang Yuan — 2 papers, h 1

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
20132019
most citedConvergence Analysis of Two-layer Neural Networks with ReLU Activation

270 citations · 862 across the 16 of their papers we have counts for

collaborators
Showing 2015Show all

4 papers · 1 filter

math.OC2015★ 100 cited

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Zeyuan Allen-Zhu, Zheng Qu, Peter Richtárik +1

Accelerated coordinate descent is widely used in optimization due to its cheap per-iteration cost and scalability to large-scale problems. Up to a primal-dual transformation, it is…

cs.GT2015★ 16 cited

Optimal Auctions vs. Anonymous Pricing

Saeed Alaei, Jason Hartline, Rad Niazadeh +2

For selling a single item to agents with independent but non-identically distributed values, the revenue optimal auction is complex. With respect to it, Hartline and Roughgarden (2…

cs.LG2015★ 26 cited

Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

Zeyuan Allen-Zhu, Yang Yuan

Many classical algorithms are found until several years later to outlive the confines in which they were conceived, and continue to be relevant in unforeseen settings. In this pape…

cs.LG2015★ 184 cited

Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition

Rong Ge, Furong Huang, Chi Jin +1

We analyze stochastic gradient descent for optimizing non-convex functions. In many cases for non-convex functions the goal is to find a reasonable local minimum, and the main conc…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.