Alessandro Rudi

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 168 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #18,996 of 35,345 by trust here, #19,439 by citations
• Statistical Learning Theory — 74 citations into it; #781 of 1,038 by trust here, #730 by citations
• Optimization & Training — 68 citations into it; #2,091 of 2,601 by trust here, #2,428 by citations
• Computational Mechanics — 65 citations into it; #5,180 of 10,001 by trust here, #4,749 by citations
• Statistics and Probability — 45 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it; #11,064 of 21,575 by trust here, #13,269 by citations
• Mathematical Physics — 27 citations into it; #5,710 of 8,562 by trust here, #4,500 by citations
• Computational Theory and Mathematics — 16 citations into it

Whom Alessandro Rudi trusts overall

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

1. Christopher K. I. Williams
2. Francis R. Bach
3. Michael W. Mahoney
4. Alex Smola
5. Radford M. Neal
6. Ingo Steinwart
7. A. Alaoui
8. Ali Rahimi
9. Andrea Caponnetto
10. Bernhard Schölkopf

Who trusts Alessandro Rudi overall

1. Luigi Carratino
2. Ulysse Marteau-Ferey
3. Francis R. Bach
4. Lorenzo Rosasco
5. Krzysztof Marcin Choromanski
6. Carlo Ciliberto
7. Zoltán Szabó
8. Gilles Blanchard
9. Felix X. Yu
10. Dmitrii Ostrovskii

Papers from Alessandro Rudi's perspective

Ordered by how strongly the researchers Alessandro Rudi 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Learning with SGD and Random Features (2018) — 87 citations
• Generalization Properties of Learning with Random Features (2016) — 382 citations
• Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses (2019) — 41 citations
• Exponential convergence of testing error for stochastic gradient methods (2017) — 36 citations
• Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance (2019) — 58 citations
• A Consistent Regularization Approach for Structured Prediction (2016) — 86 citations
• Less is More: Nystr\"om Computational Regularization (2015) — 128 citations
• Less is More: Nyström Computational Regularization (2015) — 299 citations