Tyler H. McCormick

Artificial Intelligence · 2011–2015

A researcher in the Science corpus: 9 papers; trusts 32 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #6,793 of 35,345 by trust here, #6,157 by citations
• Statistics and Probability — 41 citations into it; #12,360 of 12,932 by trust here, #12,077 by citations
• Information Systems — 18 citations into it; #4,098 of 8,771 by trust here, #6,501 by citations
• Statistical Learning Theory — 17 citations into it
• Algorithms & Complexity — 15 citations into it
• Computational Theory and Mathematics — 14 citations into it; #1,170 of 12,182 by trust here, #596 by citations
• Probabilistic & Bayesian Methods — 12 citations into it; #410 of 1,525 by trust here, #236 by citations
• Evaluation & Benchmarks — 10 citations into it

Whom Tyler H. McCormick trusts overall

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

1. Chris Fraley
2. Christian Borgelt
3. Eric R. Ziegel
4. Hugh A. Chipman
5. Adrian E. Raftery
6. Mark Newman
7. Leroy Hood
8. R. Rivest
9. Leo Breiman
10. J. Ross Quinlan

Who trusts Tyler H. McCormick overall

1. Cynthia Rudin
2. Tong Wang
3. Benjamin Letham
4. Berk Ustun
5. Elaine Angelino
6. Himabindu Lakkaraju
7. S. Uddin
8. Giulia Vilone
9. Finale Doshi-Velez
10. Osbert Bastani

Papers from Tyler H. McCormick's perspective

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

1. The Nature of Statistical Learning Theory
2. The Elements of Statistical Learning
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Regression Shrinkage and Selection via the Lasso
8. A decision-theoretic generalization of on-line learning and an application to boosting
9. Attention is All you Need (Neural Information Processing Systems)
10. Long Short-Term Memory

Their papers

• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015) — 807 citations
• Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions (2012) — 46 citations
• Modeling recovery curves with application to prostatectomy. (2015) — 6 citations
• Reactive point processes: A new approach to predicting power failures in underground electrical systems (2015) — 69 citations
• Establishing consistency and improving uncertainty estimates of variational inference through M-estimation (2015) — 15 citations
• An interpretable model for stroke prediction using rules and Bayesian analysis (2014)
• An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis (2013) — 53 citations
• A Practical Guide to Measuring Social Structure Using Indirectly Observed Network Data (2013) — 49 citations
• A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction (2011)