Daniel M. Kane

Artificial Intelligence · 2010–2019

A researcher in the Science corpus: 15 papers; trusts 67 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 129 citations into it; #3,410 of 35,345 by trust here, #3,423 by citations
• Statistics and Probability — 88 citations into it; #1,725 of 12,932 by trust here, #776 by citations
• Computational Theory and Mathematics — 41 citations into it
• Computational Mechanics — 34 citations into it; #2,368 of 10,001 by trust here, #5,157 by citations
• Statistical Learning Theory — 32 citations into it; #464 of 1,038 by trust here, #243 by citations
• Safety, Robustness & Interpretability — 21 citations into it; #1,722 of 2,251 by trust here, #1,349 by citations
• Computer Vision and Pattern Recognition — 18 citations into it; #7,357 of 21,575 by trust here, #14,478 by citations
• Applied Mathematics — 17 citations into it

Whom Daniel M. Kane trusts overall

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

1. Ilias Diakonikolas
2. Moses Charikar
3. Gregory Valiant
4. Leslie G. Valiant
5. Michael Kearns
6. Ankur Moitra
7. Nir Ailon
8. Gautam Kamath
9. Jacob Steinhardt
10. Kevin A. Lai

Who trusts Daniel M. Kane overall

1. Ilias Diakonikolas
2. David P. Woodruff
3. Leo Liberti
4. Gregory Valiant
5. Jerry Zheng Li
6. Gautam Kamath
7. Kenneth L. Clarkson
8. Jelani Nelson
9. Mert Pilanci
10. Alistair Stewart

Papers from Daniel M. Kane's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Et al
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Robust Estimators in High Dimensions without the Computational Intractability (2016) — 568 citations
• Sever: A Robust Meta-Algorithm for Stochastic Optimization (2018) — 319 citations
• Being Robust (in High Dimensions) Can Be Practical (2017) — 267 citations
• Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures (2016) — 254 citations
• List-decodable robust mean estimation and learning mixtures of spherical gaussians (2017) — 160 citations
• Robustly Learning a Gaussian: Getting Optimal Error, Efficiently (2017) — 142 citations
• Communication and Memory Efficient Testing of Discrete Distributions (2019) — 31 citations
• Learning geometric concepts with nasty noise (2017) — 108 citations
• Robust Learning of Fixed-Structure Bayesian Networks (2016) — 47 citations
• Efficient Robust Proper Learning of Log-concave Distributions (2016) — 30 citations
• Testing Identity of Structured Distributions (2014) — 110 citations
• Modeling the distribution of ranks, Selmer groups, and Shafarevich-Tate groups of elliptic curves (2013) — 93 citations
• On the ranks of the 2-Selmer groups of twists of a given elliptic curve (2010) — 64 citations
• An optimal algorithm for the distinct elements problem (2010) — 376 citations
• Sparser Johnson-Lindenstrauss Transforms (2010) — 397 citations