Zhuoran Yang

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 10 papers; trusts 367 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 388 citations into it; #4,995 of 35,345 by trust here, #6,720 by citations
• Reinforcement Learning — 371 citations into it; #1,383 of 2,242 by trust here, #1,778 by citations
• Management Science and Operations Research — 146 citations into it; #1,157 of 11,946 by trust here, #1,207 by citations
• Computational Theory and Mathematics — 70 citations into it; #3,856 of 12,182 by trust here, #2,201 by citations
• Computer Networks and Communications — 69 citations into it; #3,949 of 9,886 by trust here, #3,228 by citations
• Optimization & Training — 68 citations into it
• Game & Decision Theory — 53 citations into it
• Economics and Econometrics — 50 citations into it

Whom Zhuoran Yang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Csaba Szepesvari
3. R´emi Munos
4. Michael L. Littman
5. Geoffrey E. Hinton
6. Andrew Barto
7. David Silver
8. Sham M. Kakade
9. Ronald J. Williams
10. Peter L. Bartlett

Who trusts Zhuoran Yang overall

1. Qi Cai
2. Alekh Agarwal
3. Afshin Oroojlooyjadid
4. Qiaomin Xie
5. Ruosong Wang
6. Joshua Achiam
7. Masatoshi Uehara
8. Zhaoran Wang
9. Xin Chen
10. Jieyi Long

Papers from Zhuoran Yang's perspective

Ordered by how strongly the researchers Zhuoran Yang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Is Pessimism Provably Efficient for Offline RL? (2020) — 437 citations
• Provably Efficient Exploration in Policy Optimization (2019) — 306 citations
• Provably Efficient Reinforcement Learning with Linear Function Approximation (2019) — 700 citations
• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019) — 1,692 citations
• A Theoretical Analysis of Deep Q-Learning (2019) — 772 citations
• Sample-Efficient Reinforcement Learning for POMDPs with Linear Function Approximations (2022) — 9 citations
• Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (2018) — 685 citations
• Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium (2020) — 144 citations
• A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning (2019) — 72 citations
• Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space (2018) — 240 citations