Alexander R. Statnikov

Artificial Intelligence · 2003–2019

A researcher in the Science corpus: 13 papers; trusts 47 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #6,721 of 35,345 by trust here, #6,796 by citations
• Molecular Biology — 49 citations into it; #4,589 of 17,773 by trust here, #5,968 by citations
• Optimization & Training — 33 citations into it
• Statistics and Probability — 32 citations into it; #8,856 of 12,932 by trust here, #4,253 by citations
• Computational Theory and Mathematics — 29 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Probabilistic & Bayesian Methods — 22 citations into it
• Information Systems — 20 citations into it

Whom Alexander R. Statnikov trusts overall

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

1. Ioannis Tsamardinos
2. Constantin F. Aliferis
3. Tom Fawcett
4. Yoav Benjamini
5. Bernhard Schölkopf
6. Tom L. Burr
7. Rong-En Fan
8. Judea Pearl
9. Isabelle M Guyon
10. Ron Kohavi

Who trusts Alexander R. Statnikov overall

1. Constantin F. Aliferis
2. Ioannis Tsamardinos
3. Austin H. Chen
4. Isabelle M Guyon
5. Diviyan Kalainathan
6. Lu Zhang
7. Edoardo Pasolli
8. Jan Lemeire
9. Zone-Wei Huang
10. Miron Bartosz Kursa

Papers from Alexander R. Statnikov's perspective

Ordered by how strongly the researchers Alexander R. Statnikov 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Regression Shrinkage and Selection via the Lasso
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis (2004) — 767 citations
• New methods for separating causes from effects in genomics data (2012) — 37 citations
• Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010) — 611 citations
• A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification (2008) — 690 citations
• Time and sample efficient discovery of Markov blankets and direct causal relations (2003) — 406 citations
• GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data (2005) — 199 citations
• A comprehensive evaluation of multicategory classification methods for microbiomic data (2013) — 194 citations
• Analysis of the AutoML Challenge Series 2015-2018 (2019) — 189 citations
• A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention (2016) — 84 citations
• Design of the 2015 ChaLearn AutoML challenge (2015) — 135 citations
• Algorithms for Discovery of Multiple Markov Boundaries (2013) — 89 citations
• Algorithms for Large Scale Markov Blanket Discovery (2003) — 632 citations
• HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection (2003) — 383 citations