Yu Bai

Artificial Intelligence · with Jin · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 124 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #9,937 of 35,345 by trust here, #12,580 by citations
• Reinforcement Learning — 57 citations into it; #1,171 of 2,242 by trust here, #1,895 by citations
• Management Science and Operations Research — 43 citations into it; #4,441 of 11,946 by trust here, #9,931 by citations
• Statistical Learning Theory — 36 citations into it; #708 of 1,038 by trust here, #519 by citations
• Optimization & Training — 35 citations into it; #1,667 of 2,601 by trust here, #1,509 by citations
• Scaling & Learning Theory — 26 citations into it; #382 of 428 by trust here, #384 by citations
• Statistics and Probability — 25 citations into it; #7,272 of 12,932 by trust here, #4,014 by citations
• Computational Mechanics — 21 citations into it; #5,319 of 10,001 by trust here, #3,463 by citations

Whom Yu Bai trusts overall

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

1. Simon Shaolei Du
2. Zeyuan Allen-Zhu
3. Jason D. Lee
4. Chi Jin
5. Yuanzhi Li
6. Lénaïc Chizat
7. Geoffrey E. Hinton
8. Sanjeev Arora
9. Joel A. Tropp
10. Francis R. Bach

Who trusts Yu Bai overall

1. Aadirupa Saha
2. Yunru Bai
3. Qinghua Liu
4. Tiancheng Yu
5. Chi Jin
6. Akshay Krishnamurthy
7. Eugene A. Golikov
8. Samet Oymak
9. Yuejie Chi
10. Aldo Pacchiano

Papers from Yu Bai's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The landscape of empirical risk for nonconvex losses (2016) — 333 citations
• Near-Optimal Offline Reinforcement Learning via Double Variance Reduction (2021) — 71 citations
• Provable Self-Play Algorithms for Competitive Reinforcement Learning (2020) — 173 citations
• A Sharp Analysis of Model-based Reinforcement Learning with Self-Play (2020) — 141 citations
• Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks (2019) — 130 citations