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Ziyun Wang

35 papers hereh-index 3421.6k citations56 works total

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

author position
  • first author9
  • middle author21
  • last author3

Across the 33 of 35 papers where every author was matched, so the position is known.

fields
  • cs.LG17
  • stat.ML5
  • cs.CV3
  • cs.RO3
  • stat.CO3
  • cs.AI2
same name
  • ZiYun Wang — 14 papers, h 34
  • Ziyun Wang — 10 papers, h 6
  • Ziyun Wang — 2 papers, h 2
  • Ziyun Wang — 1 paper, h 41
  • Ziyun Wang — 1 paper, h 4
  • Ziyun Wang — 1 paper, h 5

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
20112021
most citedDueling Network Architectures for Deep Reinforcement Learning

1.8k citations · 3.8k across the 35 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

stat.ML2021

Quasi-Bayesian Dual Instrumental Variable Regression

Ziyu Wang, Yuhao Zhou, Tongzheng Ren +1

Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quant…

cs.LG2021★ 9 cited

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

Michael R. Zhang, Tom Le Paine, Ofir Nachum +4

Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action…

cs.LG2021★ 23 cited

Benchmarks for Deep Off-Policy Evaluation

Justin Fu, Mohammad Norouzi, Ofir Nachum +10

Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making. The ability…

cs.LG2021★ 4 cited

Regularized Behavior Value Estimation

Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang +7

Offline reinforcement learning restricts the learning process to rely only on logged-data without access to an environment. While this enables real-world applications, it also pose…

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