Marco Virgolin

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom Marco Virgolin trusts

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

1. Cynthia Rudin (in Artificial Intelligence)
2. Jaka Demsar (in Evaluation & Benchmarks)
3. Ronald J. Williams (in Reinforcement Learning)

Papers from Marco Virgolin's perspective

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

1. ImageNet: A large-scale hierarchical image database
2. Genetic Programming: On the Programming of Computers by Means of Natural Selection
3. Scikit-learn: Machine Learning in Python
4. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
5. XGBoost: A Scalable Tree Boosting System
6. Distilling Free-Form Natural Laws from Experimental Data
7. Greedy function approximation: A gradient boosting machine.
8. Random Forests
9. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
10. LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Their papers

• Contemporary Symbolic Regression Methods and their Relative Performance (2021) — 418 citations