Andrea Zanette

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #6,175 of 35,345 by trust here, #17,412 by citations
• Reinforcement Learning — 32 citations into it; #745 of 2,242 by trust here, #1,663 by citations
• Management Science and Operations Research — 24 citations into it; #1,277 of 11,946 by trust here, #3,825 by citations
• Computer Networks and Communications — 9 citations into it
• Statistical Learning Theory — 9 citations into it; #395 of 1,038 by trust here, #662 by citations
• Statistics and Probability — 6 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Andrea Zanette trusts overall

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

1. R´emi Munos
2. Nan Jiang
3. Richard S. Sutton
4. Csaba Szepesvari
5. Lihong Li
6. Sham M. Kakade
7. Tor Lattimore
8. Tengyang Xie
9. Michail G. Lagoudakis
10. Philip S. Thomas

Who trusts Andrea Zanette overall

1. Ruosong Wang
2. Yu Bai
3. Chi Jin
4. Tengyang Xie
5. Omar Darwiche Domingues
6. Simon Shaolei Du
7. Weitong Zhang
8. Niladri S. Chatterji
9. Yichong Xu
10. Ming Yin

Papers from Andrea Zanette's perspective

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

Their papers

• Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds (2019) — 310 citations
• Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL (2020) — 75 citations