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Cathy Wu

8 papers hereh-index 161.7k citations39 works total

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

author position
  • first author1
  • middle author1
  • last author6

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • eess.SY2
  • cs.AI1
  • cs.CY1
same name
  • Cathy Wu — 14 papers, h 5
  • Cathy Wu — 9 papers, h 4
  • Cathy Wu — 7 papers, h 3
  • Cathy Wu — 6 papers, h 1
  • Cathy Wu — 4 papers, h 3
  • Cathy Wu — 4 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
20182026
most citedReinforcement Learning for Mixed Autonomy Intersections

22 citations · 31 across the 7 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023

PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems

Aamir Hasan, Neeloy Chakraborty, Haonan Chen +3

Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these…

cs.LG2022★ 4 cited

The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

Vindula Jayawardana, Catherine Tang, Sirui Li +2

Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of scientific progress of the field. Beyond designing DRL methods for general intelligence, designing…

cs.LG2021★ 2 cited

Learning to Delegate for Large-scale Vehicle Routing

Sirui Li, Zhongxia Yan, Cathy Wu

Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical applications. While previous heuristic or learning-based works achieve decent solutions o…

cs.LG2018

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Cathy Wu, Aravind Rajeswaran, Yan Duan +5

Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exa…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.