Anirban Bhattacharya

Statistics and Probability · Biometrika · 2011–2017

A researcher in the Science corpus: 4 papers; trusts 55 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 71 citations into it; #2,431 of 12,932 by trust here, #4,014 by citations
• Artificial Intelligence — 48 citations into it; #8,647 of 35,345 by trust here, #21,878 by citations
• Statistical Learning Theory — 17 citations into it
• Computational Mechanics — 11 citations into it
• Management Science and Operations Research — 8 citations into it
• Control and Systems Engineering — 7 citations into it
• Optimization & Training — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Anirban Bhattacharya trusts overall

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

1. Peter L. Bartlett
2. Victor Chernozhukov
3. Vladimir Koltchinskii
4. Robert Tibshirani
5. Hedibert Freitas Lopes
6. Andrew Gelman
7. Bradley Efron
8. David A. McAllester
9. Subhashis Ghosal
10. A. van der Vaart

Who trusts Anirban Bhattacharya overall

1. Daniele Durante
2. Florian Huber
3. Shiwen Zhao
4. Pierre Alquier
5. Stéphanie van der Pas
6. Otso Ovaskainen
7. Jim E. Griffin
8. N. Abrego
9. Gleb Tikhonov
10. Martin Feldkircher

Papers from Anirban Bhattacharya's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. On Model Selection Consistency of Lasso
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. Estimating the Dimension of a Model
9. Weak Convergence and Empirical Processes: With Applications to Statistics
10. Spline Models for Observational Data

Their papers

• Dirichlet-Laplace priors for optimal shrinkage (2014) — 479 citations
• Sparse Bayesian infinite factor models. (2011) — 519 citations
• Bayesian fractional posteriors (2016) — 133 citations
• Frequentist coverage and sup-norm convergence rate in Gaussian process regression (2017) — 59 citations