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Lirong Xia

22 papers hereh-index 11358 citations39 works total

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

author position
  • sole author2
  • first author1
  • middle author6
  • last author12

Across the 21 of 22 papers where every author was matched, so the position is known.

fields
  • cs.GT15
  • cs.LG4
  • cs.AI1
  • cs.CR1
  • cs.MA1
same name
  • Lirong Xia — 34 papers, h 37
  • Lirong Xia — 7 papers, h 1
  • Lirong Xia — 5 papers
  • Lirong Xia — 4 papers, h 2
  • Lirong Xia — 4 papers, h 2
  • Lirong Xia — 2 papers, h 1

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
20162026
most citedEquitable Allocations of Indivisible Chores

12 citations · 41 across the 14 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

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.LG2020

Learning Mixtures of Random Utility Models with Features from Incomplete Preferences

Zhibing Zhao, Ao Liu, Lirong Xia

Random Utility Models (RUMs), which subsume Plackett-Luce model (PL) as a special case, are among the most popular models for preference learning. In this paper, we consider RUMs w…

cs.LG2020★ 2 cited

Dual Learning: Theoretical Study and an Algorithmic Extension

Zhibing Zhao, Yingce Xia, Tao Qin +2

Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc. The high-level idea of dual lea…

cs.LG2019★ 8 cited

Learning Mixtures of Plackett-Luce Models from Structured Partial Orders

Zhibing Zhao, Lirong Xia

Mixtures of ranking models have been widely used for heterogeneous preferences. However, learning a mixture model is highly nontrivial, especially when the dataset consists of part…

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