Daniel Alabi

Artificial Intelligence · with Angelino · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #13,009 of 35,345 by trust here, #13,760 by citations
• Statistical Learning Theory — 17 citations into it
• Information Systems — 16 citations into it; #2,426 of 8,771 by trust here, #2,307 by citations
• Computational Theory and Mathematics — 12 citations into it; #3,883 of 12,182 by trust here, #2,565 by citations
• Algorithms & Complexity — 10 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Statistics and Probability — 8 citations into it
• Medical & Health AI — 5 citations into it

Whom Daniel Alabi trusts overall

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

1. Cynthia Rudin
2. Hugh A. Chipman
3. J. Ross Quinlan
4. Niko Speybroeck
5. R. Rivest
6. William W. Cohen
7. Berk Ustun
8. Nicholas Larus-Stone
9. Tong Wang
10. Hongyu Yang

Who trusts Daniel Alabi overall

1. Cynthia Rudin
2. Alexey Ignatiev
3. Tong Wang
4. S. Lo Piano
5. Nina Narodytska
6. Lesia Semenova

Papers from Daniel Alabi's perspective

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

1. C4.5: Programs for Machine Learning
2. Classification and regression trees
3. Learning decision lists
4. Programs for Machine Learning
5. The CN2 Induction Algorithm
6. Bayesian CART Model Search
7. Falling Rule Lists
8. Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
9. ImageNet classification with deep convolutional neural networks
10. Integrating Classification and Association Rule Mining

Their papers

• Learning Certifiably Optimal Rule Lists (2017) — 201 citations
• Systems Optimizations for Learning Certifiably Optimal Rule Lists (2018)