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Rui Wu

4 papers hereh-index 4606 citations6 works total

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

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG4
same name
  • Rui Wu — 3 papers, h 5
  • Rui Wu — 2 papers, h 2
  • Rui Wu — 2 papers
  • Rui Wu — 1 paper
  • Rui Wu — 1 paper
  • Rui Wu — 1 paper

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
20192022
most citedConditional Generation Net for Medication Recommendation

118 citations · 193 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2022★ 118 cited

Conditional Generation Net for Medication Recommendation

Rui Wu, Zhaopeng Qiu, Jiacheng Jiang +2

Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. Currently, the recommendation is manua…

cs.LG2020★ 1 cited

Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing

Ge Liu, Rui Wu, Heng-Tze Cheng +7

Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments. However, training deep RL model is challenging in real world applications such as…

cs.LG2019★ 50 cited

RecSim: A Configurable Simulation Platform for Recommender Systems

Eugene Ie, Chih-wei Hsu, Martin Mladenov +5

We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim all…

cs.LG2019★ 24 cited

Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology

Eugene Ie, Vihan Jain, Jing Wang +10

Most practical recommender systems focus on estimating immediate user engagement without considering the long-term effects of recommendations on user behavior. Reinforcement learni…

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