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researcher

Li Zhao

21 papers hereh-index 211.9k citations39 works total

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

author position
  • middle author21

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.LG9
  • cs.AI8
  • cs.CL1
  • cs.IR1
  • q-fin.ST1
  • stat.ML1
same name
  • Li Zhao — 40 papers, h 18
  • Li Zhao — 33 papers, h 12
  • Li Zhao — 21 papers, h 7
  • Li Zhao — 21 papers, h 13
  • Li Zhao — 9 papers, h 6
  • Li Zhao — 9 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
20172023
most citedReinforcement Learning for Relation Classification from Noisy Data

183 citations · 444 across the 21 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021★ 1 cited

Curriculum Offline Imitation Learning

Minghuan Liu, Hanye Zhao, Zhengyu Yang +4

Offline reinforcement learning (RL) tasks require the agent to learn from a pre-collected dataset with no further interactions with the environment. Despite the potential to surpas…

cs.LG2021★ 3 cited

Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning

Jongjin Park, Younggyo Seo, Chang Liu +4

Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal…

cs.LG2021★ 4 cited

Distributional Reinforcement Learning for Multi-Dimensional Reward Functions

Pushi Zhang, Xiaoyu Chen, Li Zhao +3

A growing trend for value-based reinforcement learning (RL) algorithms is to capture more information than scalar value functions in the value network. One of the most well-known m…

cs.LG2021★ 18 cited

Return-Based Contrastive Representation Learning for Reinforcement Learning

Guoqing Liu, Chuheng Zhang, Li Zhao +5

Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxi…

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