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Xiaojin Zhang

Hong Kong University of Science and Technology

15 papers hereh-index 9633 citations20 works total

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

author position
  • sole author4
  • first author9
  • middle author1
  • last author1

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG12
  • cs.DS2
  • cs.CC1
affiliations
  • Hong Kong University of Science and Technology
same name
  • Xiaojin Zhang — 25 papers, h 6
  • Xiaojin Zhang — 22 papers, h 8
  • Xiaojin Zhang — 7 papers, h 3
  • Xiaojin Zhang — 2 papers, h 1
  • Xiaojin Zhang — 2 papers, h 1
  • Xiaojin Zhang — 1 paper, h 3

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
20192023
most citedAchieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously

8 citations · 23 across the 13 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.DS2019

Improved Algorithm for Tolerant Junta Testing

Xiaojin Zhang

In this paper, we consider the problem of tolerant junta testing for boolean function. Compared with the prior work by Blais et al., we provide improved results in terms of both th…

cs.LG2019

Automatic Ensemble Learning for Online Influence Maximization

Xiaojin Zhang

We consider the problem of selecting a seed set to maximize the expected number of influenced nodes in the social network, referred to as the \textit{influence maximization} (IM) p…

cs.LG2019

Contextual Combinatorial Conservative Bandits

Xiaojin Zhang, Shuai Li, Weiwen Liu +1

The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range…

cs.CC2019★ 2 cited

Near-Linear Regimes for One-Sided Distribution-Free Junta Testing

Xiaojin Zhang

We study adaptive one-sided distribution-free testing of Boolean k-juntas, with membership-query access to the function and sample access to an arbitrary unknown distribution. Fo…

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