Alexandre Lacasse

Statistical Learning Theory · 2009–2015

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 24 citations into it; #509 of 1,038 by trust here, #531 by citations
• Artificial Intelligence — 22 citations into it; #11,112 of 35,345 by trust here, #15,866 by citations
• Management Science and Operations Research — 8 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it; #719 of 1,525 by trust here, #849 by citations
• Evaluation & Benchmarks — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Alexandre Lacasse trusts overall

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

1. David A. McAllester
2. J. Langford
3. Leo Breiman
4. Olivier Catoni
5. John Shawe-Taylor
6. Arindam Banerjee
7. Robert E. Schapire
8. Yevgeny Seldin
9. François Laviolette
10. Yoav Freund

Who trusts Alexandre Lacasse overall

1. Ben London
2. Yevgeny Seldin
3. Pierre Alquier
4. Pascal Germain
5. Anastasia Pentina
6. François Laviolette
7. Luc Bégin
8. Javen Qinfeng Shi
9. Bert Huang
10. Christoph H. Lampert

Papers from Alexandre Lacasse's perspective

Ordered by how strongly the researchers Alexandre Lacasse trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Learning Multiple Layers of Features from Tiny Images
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

Their papers

• PAC-Bayesian learning of linear classifiers (2009) — 268 citations
• Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm (2015) — 156 citations