Gersende Fort

Statistics and Probability · 2000–2014

A researcher in the Science corpus: 11 papers; trusts 54 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 47 citations into it; #1,134 of 12,932 by trust here, #1,662 by citations
• Artificial Intelligence — 37 citations into it; #7,400 of 35,345 by trust here, #15,866 by citations
• Optimization & Training — 34 citations into it; #1,979 of 2,601 by trust here, #2,259 by citations
• Mathematical Physics — 25 citations into it; #848 of 8,562 by trust here, #1,261 by citations
• Computational Mechanics — 24 citations into it; #5,934 of 10,001 by trust here, #8,258 by citations
• Probabilistic & Bayesian Methods — 20 citations into it
• Computer Networks and Communications — 18 citations into it; #4,281 of 9,886 by trust here, #5,733 by citations
• Mathematical Optimization — 12 citations into it

Whom Gersende Fort trusts overall

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

1. S. Jarner
2. Gareth O. Roberts
3. Han-Fu Chen
4. Boris Polyak
5. Sean P. Meyn
6. Richard L. Tweedie
7. Patrick L. Combettes
8. Yurii E. Nesterov
9. Peter Hall
10. Anatoli B. Juditsky

Who trusts Gersende Fort overall

1. Galin L. Jones
2. Gareth O. Roberts
3. Oleg Butkovsky
4. Hiroki Masuda
5. R. Neath
6. S. Jarner
7. Fredrik Lindsten
8. Arnaud Guillin
9. Randal Douc
10. Eric Moulines

Papers from Gersende Fort's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. A Stochastic Approximation Method
3. Markov Chains and Stochastic Stability
4. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. ImageNet classification with deep convolutional neural networks
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. A method for solving the convex programming problem with convergence rate O(1/k^2)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• On the geometric ergodicity of hybrid samplers (2003) — 57 citations
• Polynomial ergodicity of Markov transition kernels (2003) — 72 citations
• Performance of a Distributed Stochastic Approximation Algorithm (2012) — 117 citations
• Practical drift conditions for subgeometric rates of convergence (2004) — 189 citations
• Convergence of the Monte Carlo expectation maximization for curved exponential families (2003) — 159 citations
• V-Subgeometric ergodicity for a Hastings–Metropolis algorithm (2000) — 58 citations
• Subgeometric rates of convergence of f-ergodic strong Markov processes (2006) — 199 citations
• SUBGEOMETRIC ERGODICITY OF STRONG MARKOV PROCESSES (2005) — 104 citations
• On stochastic proximal gradient algorithms (2014) — 48 citations
• On Perturbed Proximal Gradient Algorithms (2014) — 115 citations
• Central Limit Theorems for Stochastic Approximation with controlled Markov chain dynamics (2013) — 54 citations