Ronald Ortner

Artificial Intelligence · 2006–2018

A researcher in the Science corpus: 7 papers; trusts 51 researchers and is trusted by 122 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #1,813 of 35,345 by trust here, #4,101 by citations
• Reinforcement Learning — 48 citations into it; #235 of 2,242 by trust here, #455 by citations
• Management Science and Operations Research — 39 citations into it; #210 of 11,946 by trust here, #352 by citations
• Statistical Learning Theory — 22 citations into it
• Computer Networks and Communications — 14 citations into it; #914 of 9,886 by trust here, #2,424 by citations
• Statistics and Probability — 13 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Ronald Ortner trusts overall

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

1. Peter Auer
2. Peter L. Bartlett
3. Michael Kearns
4. Daniel Paulin
5. Alexander L. Strehl
6. Nicolò Cesa-Bianchi
7. Ronen I. Brafman
8. Thomas Jaksch
9. Ambuj Tewari
10. Satinder Singh

Who trusts Ronald Ortner overall

1. Ian Osband
2. Tor Lattimore
3. Kamyar Azizzadenesheli
4. Benjamin Van Roy
5. Emma Brunskill
6. Peter L. Bartlett
7. Romain Laroche
8. Christoph Dann
9. Csaba Szepesvari
10. Zhuoran Yang

Papers from Ronald Ortner's perspective

Ordered by how strongly the researchers Ronald Ortner 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. Near-optimal Regret Bounds for Reinforcement Learning
9. Learning Multiple Layers of Features from Tiny Images
10. Asymptotically Efficient Adaptive Allocation Rules

Their papers

• Near-optimal Regret Bounds for Reinforcement Learning (2008) — 1,579 citations
• Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning (2006) — 272 citations
• UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem (2010) — 396 citations
• Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning (2018) — 129 citations
• Regret Bounds for Reinforcement Learning via Markov Chain Concentration (2018) — 50 citations
• Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (2015) — 41 citations
• PAC-Bayesian Analysis of Contextual Bandits (2011) — 48 citations