8 papers · 1 filter
ORACLE: Optimizer-Relative Alignment for Constrained LEarning
Utkarsh Grover, Wyatt Mackey, Kaixun Hua +2
Constraint handling methods typically intervene before the optimizer acts, by modifying the objective or the gradient. Yet momentum, adaptive scaling, and structured preconditionin…
Graph4BiLO: Graph Neural Network Approximation for Bilevel Mixed-Integer Linear Optimization
Jessica D. Elrefaei, Kaixun Hua, Seungbae Kim +2
Bilevel mixed-integer linear optimization problems model hierarchical decision processes in which a leader anticipates the optimal response of a follower. Although expressive, thes…
Which Directions Matter? Sparse Design for Affine Robust Optimization
Pedro Chumpitaz-Flores, My Duong, Juan S. Borrero +1
Robust machine learning and optimization rely on the uncertainty model choice. We investigate which uncertainty directions a model must cover when defined by a finite dictionary an…
PASS: Certified Subset Repair for Classical and Quantum Pairwise Constrained Clustering
Pedro Chumpitaz-Flores, My Duong, Ying Mao +1
Pairwise-constrained clustering incorporates side information through must-link (ML) and cannot-link (CL) relations between samples. While these constraints can improve cluster qua…
QIBONN: A Quantum-Inspired Bilevel Optimizer for Neural Networks on Tabular Classification
Pedro Chumpitaz-Flores, My Duong, Ying Mao +1
Hyperparameter optimization (HPO) for neural networks on tabular data is critical to a wide range of applications, yet it remains challenging due to large, non-convex search spaces…
RS-ORT: A Reduced-Space Branch-and-Bound Algorithm for Optimal Regression Trees
Cristobal Heredia, Pedro Chumpitaz-Flores, Kaixun Hua
Mixed-integer programming (MIP) has emerged as a powerful framework for learning optimal decision trees. Yet, existing MIP approaches for regression tasks are either limited to pur…
A Scalable Global Optimization Algorithm For Constrained Clustering
Pedro Chumpitaz-Flores, My Duong, Cristobal Heredia +1
Constrained clustering leverages limited domain knowledge to improve clustering performance and interpretability, but incorporating pairwise must-link and cannot-link constraints i…
SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes
Xuyuan Xiong, Pedro Chumpitaz-Flores, Kaixun Hua +1
Interpretable reinforcement learning policies are essential for high-stakes decision-making, yet optimizing decision tree policies in Markov Decision Processes (MDPs) remains chall…