Jean-Francis Roy

Artificial Intelligence · 2014–2016

A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Statistical Learning Theory — 34 citations into it
• Management Science and Operations Research — 14 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it; #1,425 of 1,525 by trust here, #1,460 by citations
• Statistics and Probability — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Jean-Francis Roy trusts overall

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

1. David A. McAllester
2. Yuhai Wu
3. J. Langford
4. Arindam Banerjee
5. Yevgeny Seldin
6. François Laviolette
7. Olivier Catoni
8. Pascal Germain
9. Leo Breiman
10. John Shawe-Taylor

Who trusts Jean-Francis Roy overall

1. Pierre Alquier

Papers from Jean-Francis Roy's perspective

Ordered by how strongly the researchers Jean-Francis Roy trusts endorse each paper. Same corpus, a different order for every researcher.

1. Support-Vector Networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Bagging Predictors
4. Information Theory: 1948-1998 - Guest Editorial
5. Statistical Learning Theory
6. Some PAC-Bayesian Theorems
7. PAC-Bayesian Stochastic Model Selection
8. ImageNet classification with deep convolutional neural networks
9. Boosting the margin: A new explanation for the effectiveness of voting methods
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• PAC-Bayesian Bounds based on the Rényi Divergence (2016) — 96 citations
• Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm (2015) — 156 citations
• PAC-Bayesian Theory for Transductive Learning (2014) — 52 citations