Emilie Kaufmann

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 11 papers; trusts 52 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #5,207 of 35,345 by trust here, #6,517 by citations
• Management Science and Operations Research — 79 citations into it; #962 of 11,946 by trust here, #760 by citations
• Reinforcement Learning — 61 citations into it; #425 of 2,242 by trust here, #805 by citations
• Statistics and Probability — 27 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Statistical Learning Theory — 19 citations into it; #755 of 1,038 by trust here, #342 by citations
• Electrical and Electronic Engineering — 12 citations into it; #1,200 of 16,539 by trust here, #6,657 by citations
• Computer Networks and Communications — 11 citations into it; #4,788 of 9,886 by trust here, #8,844 by citations

Whom Emilie Kaufmann trusts overall

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

1. Tze Leung Lai
2. Aurélien Garivier
3. T. L. Lai Andherbertrobbins
4. Peter Auer
5. Olivier Capp'e
6. R´emi Munos
7. Sébastien Bubeck
8. Martin L. Puterman
9. Shie Mannor
10. Jean-Yves Audibert

Who trusts Emilie Kaufmann overall

1. Daniel Russo
2. Shipra Agrawal
3. Aurélien Garivier
4. R´emi Munos
5. Tor Lattimore
6. Brendan O'Donoghue
7. Raphaël Féraud
8. Navin Goyal
9. Matthew D. Hoffman
10. Bobak Shahriari

Papers from Emilie Kaufmann's perspective

Ordered by how strongly the researchers Emilie Kaufmann 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. Some aspects of the sequential design of experiments
8. GENERATIVE ADVERSARIAL NETS
9. Long Short-Term Memory
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis (2012) — 619 citations
• A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces (2020) — 45 citations
• On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models (2014) — 1,103 citations
• Optimal Best Arm Identification with Fixed Confidence (2016) — 421 citations
• Machine learning applications in drug development (2019) — 184 citations
• Learning the distribution with largest mean: two bandit frameworks (2017) — 21 citations
• Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling (2018) — 28 citations
• Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited (2020) — 118 citations
• Regret Bounds for Kernel-Based Reinforcement Learning (2020) — 22 citations
• Information Complexity in Bandit Subset Selection (2013) — 195 citations
• On Bayesian Upper Confidence Bounds for Bandit Problems (2012) — 414 citations