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researcher

Jian Li

13 papers hereh-index 212.4k citations45 works total

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

author position
  • first author3
  • middle author6
  • last author4

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.DS4
  • cs.LG4
  • math.OC2
  • cs.CL1
  • cs.MM1
  • cs.SI1
same name
  • Jian Li — 53 papers, h 17
  • Jian Li — 50 papers, h 28
  • Jian Li — 47 papers, h 19
  • Jian Li — 33 papers, h 13
  • Jian Li — 30 papers, h 31
  • Jian Li — 19 papers, h 12

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
20122021
most citedNetwork Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

722 citations · 998 across the 13 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2020★ 16 cited

Improved Algorithms for Convex-Concave Minimax Optimization

Yuanhao Wang, Jian Li

This paper studies minimax optimization problems minx​maxy​f(x,y), where f(x,y) is mx​-strongly convex with respect to x, my​-strongly concave with respect to y and…

cs.LG2018★ 10 cited

Gradient Boosting With Piece-Wise Linear Regression Trees

Yu Shi, Jian Li, Zhize Li

Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training al…

cs.LG2016★ 9 cited

Pure Exploration of Multi-armed Bandit Under Matroid Constraints

Lijie Chen, Anupam Gupta, Jian Li

We study the pure exploration problem subject to a matroid constraint (Best-Basis) in a stochastic multi-armed bandit game. In a Best-Basis instance, we are given n stochastic ar…

cs.LG2015★ 2 cited

Learning Arbitrary Statistical Mixtures of Discrete Distributions

Jian Li, Yuval Rabani, Leonard J. Schulman +1

We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, ϑ, is a proba…

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