Steve Hanneke

Artificial Intelligence · 2006–2020

A researcher in the Science corpus: 9 papers; trusts 86 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #3,261 of 35,345 by trust here, #8,999 by citations
• Statistics and Probability — 40 citations into it
• Statistical Learning Theory — 29 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Scaling & Learning Theory — 17 citations into it
• Computational Theory and Mathematics — 16 citations into it; #1,092 of 12,182 by trust here, #2,851 by citations
• Computational Mechanics — 14 citations into it
• Control and Systems Engineering — 12 citations into it

Whom Steve Hanneke trusts overall

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

1. Peter L. Bartlett
2. David Haussler
3. Sanjoy Dasgupta
4. Alexandre B. Tsybakov
5. Maria-Florina Balcan
6. Martin H. Anthony
7. Sally A. Goldman
8. Vladimir Naumovich Vapnik
9. Michael Kearns
10. Vladimir Koltchinskii

Who trusts Steve Hanneke overall

1. Maria-Florina Balcan
2. Sanjoy Dasgupta
3. Philip M. Long
4. Rahul Ramesh
5. Daniel J. Hsu
6. Alekh Agarwal
7. Ozan Sener
8. Pranjal Awasthi
9. Alberto Bietti
10. Skyler J. Cranmer

Papers from Steve Hanneke's perspective

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

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

Their papers

• A theory of transfer learning with applications to active learning (2012) — 184 citations
• On the Value of Target Data in Transfer Learning (2020) — 87 citations
• Theory of Disagreement-Based Active Learning (2014) — 306 citations
• A bound on the label complexity of agnostic active learning (2007) — 326 citations
• Rates of convergence in active learning (2011) — 162 citations
• The true sample complexity of active learning (2010) — 190 citations
• A No-Free-Lunch Theorem for MultiTask Learning (2020) — 45 citations
• A compression technique for analyzing disagreement-based active learning (2014) — 21 citations
• Discrete Temporal Models of Social Networks (2006) — 578 citations