Sivaraman Balakrishnan
Statistics and Probability · 2011–2021
A researcher in the Science corpus: 14 papers; trusts 245 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 221 citations into it; #2,370 of 12,932 by trust here, #1,989 by citations
- Artificial Intelligence — 112 citations into it; #9,616 of 35,345 by trust here, #15,236 by citations
- Computational Mechanics — 53 citations into it
- Statistical Learning Theory — 49 citations into it
- Computational Theory and Mathematics — 40 citations into it
- Molecular Biology — 40 citations into it; #5,718 of 17,773 by trust here, #7,124 by citations
- Applied Mathematics — 39 citations into it
- Computer Vision and Pattern Recognition — 36 citations into it
Whom Sivaraman Balakrishnan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alexandre B. Tsybakov
- John Thomas
- Robert Tibshirani
- Iain M. Johnstone
- Bernhard Schölkopf
- Vladimir Koltchinskii
- E.J. Candes
- Martin J. Wainwright
- Jerome H. Friedman
- Gregory Valiant
Who trusts Sivaraman Balakrishnan overall
- Sergey Ovchinnikov
- Larry A. Wasserman
- Ilias Diakonikolas
- Matey Neykov
- Ilmun Kim
- Jayadev Acharya
- Aaditya Ramdas
- Yanyao Shen
- Hetunandan Kamisetty
- Roshan Rao
Papers from Sivaraman Balakrishnan's perspective
Ordered by how strongly the researchers Sivaraman Balakrishnan trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Regression Shrinkage and Selection via the Lasso
Their papers
- Learning generative models for protein fold families (2011) — 341 citations
- Statistical guarantees for the EM algorithm: From population to sample-based analysis (2014) — 540 citations
- Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence (2015) — 187 citations
- Hypothesis Testing for High-Dimensional Multinomials: A Selective Review (2017) — 68 citations
- Minimax optimal conditional independence testing (2020) — 54 citations
- Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates (2017) — 56 citations
- Robust estimation via robust gradient estimation (2018) — 238 citations
- Robust multivariate nonparametric tests via projection averaging (2018) — 75 citations
- Plugin estimation of smooth optimal transport maps (2021) — 135 citations
- Semiparametric Counterfactual Density Estimation (2021) — 79 citations
- Minimax optimality of permutation tests (2020) — 68 citations
- Computationally Efficient Robust Sparse Estimation in High Dimensions (2017) — 132 citations
- Optimal kernel choice for large-scale two-sample tests (2012) — 764 citations
- Finding and Leveraging Structure in Learning Problems (2012)