Ramon van Handel
Statistics and Probability · 2004–2022
A researcher in the Science corpus: 15 papers; trusts 59 researchers and is trusted by 38 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 65 citations into it; #1,299 of 12,932 by trust here, #1,829 by citations
- Mathematical Physics — 47 citations into it; #6,825 of 8,562 by trust here, #4,500 by citations
- Applied Mathematics — 28 citations into it; #5,165 of 7,620 by trust here, #5,097 by citations
- Statistical Learning Theory — 18 citations into it
- Artificial Intelligence — 17 citations into it; #8,615 of 35,345 by trust here, #13,544 by citations
- Finance — 17 citations into it
- Discrete Mathematics and Combinatorics — 15 citations into it; #1,353 of 2,017 by trust here, #1,251 by citations
- Statistical and Nonlinear Physics — 15 citations into it
Whom Ramon van Handel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Roman Vershynin
- Gábor Lugosi
- Joel A. Tropp
- Afonso S. Bandeira
- Haikady N. Nagaraja
- Vladimir Koltchinskii
- Pablo A. Parrilo
- Greg W. Anderson
- Olivier Capp'e
- Terence Tao
Who trusts Ramon van Handel overall
- Justin Salez
- Amir-Reza Asadi
- Sohini Upadhyay
- Afonso S. Bandeira
- Nicole El Karoui
- Lucas Liebenwein
- John Edward Gough
- Jing Zhang
- Marcel Nutz
- Xiaolu Tan
Papers from Ramon van Handel's perspective
Ordered by how strongly the researchers Ramon van Handel trusts endorse each paper. Same corpus, a different order for every researcher.
- The concentration of measure phenomenon
- Regression Shrinkage and Selection via the Lasso
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Decoding by linear programming
- Introduction to the non-asymptotic analysis of random matrices
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Non-asymptotic theory of random matrices: extreme singular values
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Probability in High Dimension (2014) — 381 citations
- Sharp nonasymptotic bounds on the norm of random matrices with independent entries (2014) — 303 citations
- Constructing Sublinear Expectations on Path Space (2012) — 142 citations
- The dimension-free structure of nonhomogeneous random matrices (2017) — 93 citations
- The stability of conditional Markov processes and Markov chains in random environments (2008) — 65 citations
- Universality and Sharp Matrix Concentration Inequalities (2022) — 67 citations
- Structured Random Matrices (2016) — 54 citations
- On the spectral norm of Gaussian random matrices (2015) — 59 citations
- Matrix concentration inequalities and free probability (2021) — 100 citations
- Uniform time average consistency of Monte Carlo particle filters (2008) — 52 citations
- Observability and nonlinear filtering (2007) — 56 citations
- Filtering, Stability, and Robustness (2007) — 61 citations
- An Introduction to Quantum Filtering (2006) — 514 citations
- Feedback control of quantum state reduction (2004) — 307 citations
- Deterministic Dicke-state preparation with continuous measurement and control (2004) — 136 citations