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Wei Chen

Leiden institute of advanced computer science

82 papers hereh-index 5613.9k citations675 works total

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

author position
  • first author16
  • middle author36
  • last author21

Across the 73 of 82 papers where every author was matched, so the position is known.

fields
  • cs.LG16
  • cs.CE8
  • cs.CV8
  • cs.HC5
  • cs.RO5
  • cs.IT4
affiliations
  • Leiden institute of advanced computer science
ORCID 0000-0001-7875-4548
same name
  • Wei Chen — 66 papers, h 32
  • Wei Chen — 60 papers, h 35
  • Wei Chen — 52 papers, h 25
  • Wei Chen — 49 papers, h 21
  • Wei Chen — 25 papers
  • Wei Chen — 23 papers, h 17

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
20162023
most citedDeep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems

367 citations · 1.2k across the 57 of their papers we have counts for

collaborators
Showing 2023 · cs.LGShow all

4 papers · 2 filters

cs.LG2023

Multi-Fidelity Multi-Armed Bandits Revisited

Xuchuang Wang, Qingyun Wu, Wei Chen +1

We study the multi-fidelity multi-armed bandit (MF-MAB), an extension of the canonical multi-armed bandit (MAB) problem. MF-MAB allows each arm to be pulled with different costs (f…

cs.LG2023★ 3 cited

CoSDA: Continual Source-Free Domain Adaptation

Haozhe Feng, Zhaorui Yang, Hesun Chen +5

Without access to the source data, source-free domain adaptation (SFDA) transfers knowledge from a source-domain trained model to target domains. Recently, SFDA has gained populari…

cs.LG2023★ 3 cited

Contextual Combinatorial Bandits with Probabilistically Triggered Arms

Xutong Liu, Jinhang Zuo, Siwei Wang +4

We study contextual combinatorial bandits with probabilistically triggered arms (C2MAB-T) under a variety of smoothness conditions that capture a wide range of applications, suc…

cs.LG2023★ 6 cited

BAFFLE: A Baseline of Backpropagation-Free Federated Learning

Haozhe Feng, Tianyu Pang, Chao Du +3

Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical…

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