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Xiaoyu Chen

10 papers hereh-index 9437 citations19 works total

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

author position
  • first author2
  • middle author7
  • last author1

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV2
  • cs.DS1
  • cs.NI1
  • math-ph1
  • math.PR1
same name
  • Xiaoyu Chen — 8 papers
  • Xiaoyu Chen — 8 papers, h 6
  • Xiaoyu Chen — 7 papers, h 4
  • Xiaoyu Chen — 6 papers
  • Xiaoyu Chen — 6 papers
  • Xiaoyu Chen — 5 papers, h 15

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 citedDistributed Bandit Learning: Near-Optimal Regret with Efficient Communication

8 citations · 17 across the 8 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

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

Certifiably Robust Interpretation via Renyi Differential Privacy

Ao Liu, Xiaoyu Chen, Sijia Liu +2

Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of…

cs.LG2019★ 8 cited

Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication

Yuanhao Wang, Jiachen Hu, Xiaoyu Chen +1

We study the problem of regret minimization for distributed bandits learning, in which M agents work collaboratively to minimize their total regret under the coordination of a ce…

cs.LG2019

Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP

Kefan Dong, Yuanhao Wang, Xiaoyu Chen +1

A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. \cite{jin2018q} proposed a Q-learning algorithm with UC…

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