5 papers · 1 filter
How Simple Can It Get? From Interpretable Equations to Readable Rules for Financial Decision Making
Adia Lumadjeng, Ilker Birbil, Erman Acar
In regulated domains such as finance, a model that cannot be explained cannot be deployed, yet many interpretable classifiers defeat their own purpose by producing formulas with do…
ECSEL: Explainable Classification via Signomial Equation Learning
Adia Lumadjeng, Ilker Birbil, Erman Acar
We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regressi…
Explaining the Explainer: Understanding the Inner Workings of Transformer-based Symbolic Regression Models
Arco van Breda, Erman Acar
Following their success across many domains, transformers have also proven effective for symbolic regression (SR); however, the internal mechanisms underlying their generation of m…
Interpretability for Time Series Transformers using A Concept Bottleneck Framework
Angela van Sprang, Erman Acar, Willem Zuidema
Mechanistic interpretability focuses on reverse engineering the internal mechanisms learned by neural networks. We extend our focus and propose to mechanistically forward engineer…
EMERGENT: Efficient and Manipulation-resistant Matching using GFlowNets
Mayesha Tasnim, Erman Acar, Sennay Ghebreab
The design of fair and efficient algorithms for allocating public resources, such as school admissions, housing, or medical residency, has a profound social impact. In one-sided ma…