Mattia C. F. Prosperi

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 6 papers; trusts 3 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it
• Statistics and Probability — 26 citations into it
• Causal Inference & Discovery — 11 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Mattia C. F. Prosperi trusts overall

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

1. Judea Pearl
2. Tom Fawcett
3. Hirotugu Akaike
4. Eric R. Ziegel
5. William W. Cohen
6. Miguel A. Hern'an
7. Elias Bareinboim
8. James M. Robins
9. Ron Kohavi
10. Temple F. Smith

Who trusts Mattia C. F. Prosperi overall

1. Vukosi Marivate
2. Sonali Parbhoo
3. Maurizio Zazzi
4. Jasmina Bogojeska
5. Alistair E. W. Johnson

Papers from Mattia C. F. Prosperi's perspective

Ordered by how strongly the researchers Mattia C. F. Prosperi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Causality : Models , Reasoning , and Inference
2. Probabilistic reasoning in intelligent systems - networks of plausible inference
3. The Elements of Statistical Learning
4. Causal diagrams for empirical research
5. Causal inference and the data-fusion problem
6. Gradient-based learning applied to document recognition
7. Classification and Regression Trees
8. ImageNet classification with deep convolutional neural networks
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Long Short-Term Memory

Their papers

• Causal inference and counterfactual prediction in machine learning for actionable healthcare (2020) — 432 citations
• Predicting Response to Antiretroviral Treatment by Machine Learning: The EuResist Project (2012) — 59 citations
• Prediction of response to antiretroviral therapy by human experts and by the EuResist data‐driven expert system (the EVE study) (2010) — 54 citations
• Antiretroviral Therapy Optimisation without Genotype Resistance Testing: A Perspective on Treatment History Based Models (2010) — 110 citations
• Selecting anti-HIV therapies based on a variety of genomic and clinical factors (2008) — 75 citations
• Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment (2008) — 55 citations