Alexander B. Jung

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 11 citations into it
• Safety, Robustness & Interpretability — 7 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Alexander B. Jung trusts overall

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

1. Dmitry M. Malioutov
2. William W. Cohen
3. Niko Speybroeck

Papers from Alexander B. Jung's perspective

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

1. Fast Effective Rule Induction
2. Learning decision lists
3. Classification and regression trees
4. Exact Rule Learning via Boolean Compressed Sensing
5. A SAT-Based Approach to Learn Explainable Decision Sets
6. What do we need to build explainable AI systems for the medical domain?
7. MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules
8. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
9. Algorithms for Direct 0-1 Loss Optimization in Binary Classification
10. Efficient SAT-Based Encodings of Conditional Cardinality Constraints

Their papers

• Learning Explainable Decision Rules via Maximum Satisfiability (2020) — 13 citations