Subramani Mani

Artificial Intelligence · 2001–2014

A researcher in the Science corpus: 6 papers; trusts 19 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #18,699 of 35,345 by trust here, #14,718 by citations
• Statistics and Probability — 12 citations into it; #8,291 of 12,932 by trust here, #4,253 by citations
• Probabilistic & Bayesian Methods — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Optimization & Training — 6 citations into it
• Management Science and Operations Research — 5 citations into it; #9,659 of 11,946 by trust here, #10,893 by citations

Whom Subramani Mani trusts overall

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

1. David Maxwell Chickering
2. David E. Heckerman
3. Peter Spirtes
4. Thomas S. Richardson
5. Ioannis Tsamardinos
6. Richard E. Neapolitan
7. Judea Pearl
8. Constantin F. Aliferis
9. Gregory F. Cooper
10. Tom L. Burr

Who trusts Subramani Mani overall

1. Joris M. Mooij
2. Pranjul Yadav
3. Jerome Cremers
4. Alfredo Vellido
5. Pekka Parviainen
6. Michael S. Steinbach
7. Alexander R. Statnikov
8. Ricardo M. A. Silva

Papers from Subramani Mani's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Probabilistic reasoning in intelligent systems: Networks of plausible inference
3. Causality : Models , Reasoning , and Inference
4. Learning Bayesian networks: The combination of knowledge and statistical data
5. Optimal Structure Identification With Greedy Search
6. Approximating discrete probability distributions with dependence trees
7. Causation, prediction, and search
8. Causation, Prediction, and Search
9. Learning Equivalence Classes of Bayesian Network Structures
10. Controlling the false discovery rate: a practical and powerful approach to multiple testing

Their papers

• Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010) — 611 citations
• A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries (2011) — 295 citations
• A Theoretical Study of Y Structures for Causal Discovery (2006) — 68 citations
• Medical decision support using machine learning for early detection of late-onset neonatal sepsis (2014) — 186 citations
• Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. (2011) — 99 citations
• Acceptance of Rules Generated by Machine Learning among Medical Experts (2001) — 92 citations