Tor Lattimore

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 15 papers; trusts 181 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 234 citations into it; #3,622 of 35,345 by trust here, #4,888 by citations
• Reinforcement Learning — 194 citations into it; #300 of 2,242 by trust here, #560 by citations
• Management Science and Operations Research — 164 citations into it; #704 of 11,946 by trust here, #597 by citations
• Statistical Learning Theory — 54 citations into it
• Computer Networks and Communications — 51 citations into it; #7,126 of 9,886 by trust here, #6,217 by citations
• Statistics and Probability — 50 citations into it
• Optimization & Training — 32 citations into it
• Computational Theory and Mathematics — 28 citations into it; #6,443 of 12,182 by trust here, #5,046 by citations

Whom Tor Lattimore trusts overall

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

1. Peter Auer
2. Alexander L. Strehl
3. Nicolò Cesa-Bianchi
4. Sébastien Bubeck
5. Csaba Szepesvari
6. Sham M. Kakade
7. Jean-Yves Audibert
8. Richard S. Sutton
9. R´emi Munos
10. Aurélien Garivier

Who trusts Tor Lattimore overall

1. Ian Osband
2. Brendan O'Donoghue
3. Omar Darwiche Domingues
4. Andrea Zanette
5. Yichen Chen
6. Aadirupa Saha
7. Chi Jin
8. Philip Thomas
9. Marcus Hutter
10. Benjamin Van Roy

Papers from Tor Lattimore's perspective

Ordered by how strongly the researchers Tor Lattimore 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Reinforcement Learning: An Introduction
8. Attention is All you Need (Neural Information Processing Systems)
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

• Behaviour Suite for Reinforcement Learning (2019) — 212 citations
• Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning (2017) — 329 citations
• PAC Bounds for Discounted MDPs (2012) — 198 citations
• Thompson Sampling is Asymptotically Optimal in General Environments (2016) — 40 citations
• Online Learning with Gated Linear Networks (2017) — 26 citations
• A Geometric Perspective on Optimal Representations for Reinforcement Learning (2019) — 99 citations
• The Sample-Complexity of General Reinforcement Learning (2013) — 73 citations
• Universal Knowledge-Seeking Agents for Stochastic Environments (2013) — 42 citations
• Bounded Regret for Finite-Armed Structured Bandits (2014) — 70 citations
• Regret Analysis of the Anytime Optimally Confident UCB Algorithm (2016) — 28 citations
• Causal Bandits: Learning Good Interventions via Causal Inference (2016) — 197 citations
• Conservative Bandits (2016) — 110 citations
• Degenerate Feedback Loops in Recommender Systems (2019) — 247 citations
• Stochastic matrix games with bandit feedback (2020) — 8 citations
• Bandit Algorithms (2020)