Peter I. Frazier

Artificial Intelligence · 2008–2023

A researcher in the Science corpus: 9 papers; trusts 94 researchers and is trusted by 86 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #2,312 of 35,345 by trust here, #7,945 by citations
• Management Science and Operations Research — 51 citations into it; #2,861 of 11,946 by trust here, #3,047 by citations
• Computational Theory and Mathematics — 39 citations into it; #465 of 12,182 by trust here, #2,931 by citations
• Statistics and Probability — 33 citations into it; #6,548 of 12,932 by trust here, #6,552 by citations
• Optimization & Training — 24 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it
• Mathematical Optimization — 13 citations into it
• Reinforcement Learning — 9 citations into it

Whom Peter I. Frazier trusts overall

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

1. Jonas Mockus
2. Radford M. Neal
3. Harold J. Kushner
4. John K. Kruschke
5. Daniel J. Lizotte
6. Jasper Snoek
7. Donald R. Jones
8. L. Eon Bottou
9. José Miguel Hernández-Lobato
10. Ziyun Wang

Who trusts Peter I. Frazier overall

1. Warrren B Powell
2. Maximilian Balandat
3. David Eriksson
4. Matthias Poloczek
5. Ryan D. Turner
6. Daniel Russo
7. Colin White
8. Isac Arnekvist
9. Brian Karrer
10. Vu Nguyen

Papers from Peter I. Frazier's perspective

Ordered by how strongly the researchers Peter I. Frazier 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. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The Parallel Knowledge Gradient Method for Batch Bayesian Optimization (2016) — 251 citations
• The Knowledge-Gradient Policy for Correlated Normal Beliefs (2009) — 466 citations
• A Knowledge-Gradient Policy for Sequential Information Collection (2008) — 547 citations
• Warm starting Bayesian optimization (2016) — 74 citations
• Multi-Information Source Optimization (2016) — 230 citations
• A Tutorial on Bayesian Optimization (2018) — 2,412 citations
• Incentivizing exploration (2014) — 124 citations
• Bayesian Optimization (2023) — 831 citations
• Distance dependent Chinese restaurant processes (2009) — 405 citations