19 citations · 72 across the 23 of their papers we have counts for
4 papers · 1 filter
Learning with Subset Stacking
Ş. İlker Birbil, Sinan Yıldırım, Samet Çopur +1
We propose a new regression algorithm that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the…
Bolstering Stochastic Gradient Descent with Model Building
S. Ilker Birbil, Ozgur Martin, Gonenc Onay +1
Stochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rat…
Mixed-Integer Optimization with Constraint Learning
Donato Maragno, Holly Wiberg, Dimitris Bertsimas +3
We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which c…
Rule Generation for Classification: Scalability, Interpretability, and Fairness
Tabea E. Röber, Adia C. Lumadjeng, M. Hakan Akyüz +1
We introduce a new rule-based optimization method for classification with constraints. The proposed method leverages column generation for linear programming, and hence, is scalabl…