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Jie Liu

5 papers hereh-index 81.3k citations16 works total

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

author position
  • first author3
  • middle author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • stat.ML1
same name
  • Jie Liu — 51 papers, h 33
  • Jie Liu — 22 papers
  • Jie Liu — 10 papers
  • Jie Liu — 8 papers, h 7
  • Jie Liu — 8 papers, h 8
  • Jie Liu — 8 papers, h 5

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
20172022
most citedStochastic Recursive Gradient Algorithm for Nonconvex Optimization

66 citations · 70 across the 2 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 4 cited

ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency

Chuming Li, Jie Liu, Yinmin Zhang +5

Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which is the ever-changing targets at every iteration when multiple agents update their policie…

cs.LG2020

Adaptive Gradient Method with Resilience and Momentum

Jie Liu, Chen Lin, Chuming Li +4

Several variants of stochastic gradient descent (SGD) have been proposed to improve the learning effectiveness and efficiency when training deep neural networks, among which some r…

cs.LG2018

On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches

Jie Liu, Yu Rong, Martin Takac +1

This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.…

cs.LG2018

MLE-induced Likelihood for Markov Random Fields

Jie Liu, Hao Zheng

Due to the intractable partition function, the exact likelihood function for a Markov random field (MRF), in many situations, can only be approximated. Major approximation approach…

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