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Jianye Hao

66 papers hereh-index 396.2k citations203 works total

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

author position
  • first author2
  • middle author50
  • last author11

Across the 63 of 66 papers where every author was matched, so the position is known.

fields
  • cs.LG31
  • cs.AI13
  • cs.MA12
  • cs.IR4
  • cs.SE2
  • cs.CV1
same name
  • Jianye Hao — 49 papers, h 25
  • Jianye Hao — 24 papers, h 13
  • Jianye Hao — 21 papers, h 9
  • Jianye Hao — 12 papers, h 5
  • Jianye Hao — 9 papers, h 5
  • Jianye Hao — 7 papers, 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
20162026
most citedExploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain

177 citations · 995 across the 65 of their papers we have counts for

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2022★ 1 cited

A Graph-Enhanced Click Model for Web Search

Jianghao Lin, Weiwen Liu, Xinyi Dai +6

To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models…

cs.IR2022

Debiased Recommendation with User Feature Balancing

Mengyue Yang, Guohao Cai, Furui Liu +5

Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS…

cs.IR2021★ 2 cited

CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation

Xidong Feng, Chen Chen, Dong Li +3

Practical recommender systems experience a cold-start problem when observed user-item interactions in the history are insufficient. Meta learning, especially gradient based one, ca…

cs.IR2021★ 32 cited

An Adversarial Imitation Click Model for Information Retrieval

Xinyi Dai, Jianghao Lin, Weinan Zhang +7

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how use…

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