École d'été de probabilités de Saint-Flour
Statistics and Probability · 2006–2013
A researcher in the Science corpus: 6 papers; trusts 40 researchers and is trusted by 79 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 86 citations into it; #809 of 12,932 by trust here, #817 by citations
- Mathematical Physics — 44 citations into it; #2,501 of 8,562 by trust here, #1,159 by citations
- Artificial Intelligence — 40 citations into it; #3,136 of 35,345 by trust here, #5,390 by citations
- Applied Mathematics — 32 citations into it; #991 of 7,620 by trust here, #509 by citations
- Computational Theory and Mathematics — 24 citations into it; #3,216 of 12,182 by trust here, #2,251 by citations
- Computational Mechanics — 22 citations into it; #814 of 10,001 by trust here, #2,112 by citations
- Geometry and Topology — 22 citations into it; #4,716 of 7,422 by trust here, #6,497 by citations
- Statistical Learning Theory — 20 citations into it
Whom École d'été de probabilités de Saint-Flour trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Vladimir Koltchinskii
- Peter L. Bartlett
- Michel Talagrand
- JA. Sanchez
- Olivier Bousquet
- Shahar Mendelson
- Robert Tibshirani
- Alexandre B. Tsybakov
- A. van der Vaart
- E.J. Candes
Who trusts École d'été de probabilités de Saint-Flour overall
- Gábor Lugosi
- Stanislav Minsker
- Vladimir Koltchinskii
- Sara A. van de Geer
- Shahar Mendelson
- Steve Hanneke
- Francis R. Bach
- Alessandro Rinaldo
- Tianbao Yang
- Jaeho Lee
Papers from École d'été de probabilités de Saint-Flour's perspective
Ordered by how strongly the researchers École d'été de probabilités de Saint-Flour trusts endorse each paper. Same corpus, a different order for every researcher.
- Regression Shrinkage and Selection via the Lasso
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Optimal aggregation of classifiers in statistical learning
- The Dantzig selector: Statistical estimation when P is much larger than n
Their papers
- Concentration inequalities and model selection (2007) — 993 citations
- Oracle inequalities in empirical risk minimization and sparse recovery problems (2011) — 594 citations
- Differential equations driven by rough paths (2007) — 639 citations
- Topological complexity of smooth random functions (2011) — 58 citations
- Coarse geometry and randomness (2013) — 50 citations
- The Lace Expansion and its Applications (2006) — 159 citations