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Zhihong Deng

4 papers hereh-index 8345 citations51 works total

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

author position
  • first author2
  • middle author1
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.CL1
same name
  • Zhihong Deng — 19 papers, h 31
  • Zhihong Deng — 3 papers
  • Zhihong Deng — 2 papers, h 1
  • Zhihong Deng — 2 papers, h 3
  • Zhihong Deng — 1 paper, h 6
  • Zhihong Deng — 1 paper, h 4

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
20212024
most citedPessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

24 citations · 25 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2024

What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning

Zhihong Deng, Jing Jiang, Guodong Long +1

In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maxi…

cs.CL2023★ 1 cited

Prefix-Tuning Based Unsupervised Text Style Transfer

Huiyu Mai, Wenhao Jiang, Zhihong Deng

Unsupervised text style transfer aims at training a generative model that can alter the style of the input sentence while preserving its content without using any parallel data. In…

cs.LG2022★ 24 cited

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

Chenjia Bai, Lingxiao Wang, Zhuoran Yang +4

Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline…

cs.LG2021

False Correlation Reduction for Offline Reinforcement Learning

Zhihong Deng, Zuyue Fu, Lingxiao Wang +5

Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems. Most existing papers only discuss defending against out-of-d…

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