Tengyang Xie

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 6 papers; trusts 65 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #9,081 of 35,345 by trust here, #14,718 by citations
• Reinforcement Learning — 66 citations into it; #711 of 2,242 by trust here, #1,482 by citations
• Management Science and Operations Research — 20 citations into it
• Statistics and Probability — 17 citations into it; #3,566 of 12,932 by trust here, #5,126 by citations
• Statistical Learning Theory — 14 citations into it; #735 of 1,038 by trust here, #920 by citations
• Computer Networks and Communications — 8 citations into it; #2,123 of 9,886 by trust here, #4,405 by citations
• Optimization & Training — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Tengyang Xie trusts overall

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

1. R´emi Munos
2. Richard S. Sutton
3. Nan Jiang
4. Csaba Szepesvari
5. Satinder Singh
6. Sham M. Kakade
7. Lihong Li
8. Bruno Scherrer
9. Martin L. Puterman
10. Alekh Agarwal

Who trusts Tengyang Xie overall

1. Masatoshi Uehara
2. Nan Jiang
3. Andrea Zanette
4. Nathan Kallus
5. Ruosong Wang
6. Ming Yin
7. Audrey Huang
8. Martin Klissarov
9. Jiawei Huang
10. Cameron Voloshin

Papers from Tengyang Xie's perspective

Ordered by how strongly the researchers Tengyang Xie 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. Reinforcement Learning: An Introduction
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Human-level control through deep reinforcement learning

Their papers

• Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling (2019) — 199 citations
• Bellman-consistent Pessimism for Offline Reinforcement Learning (2021) — 335 citations
• Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts (2024) — 398 citations
• Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency (2021) — 60 citations
• A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting (2020) — 29 citations
• Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison (2020) — 83 citations