2 citations · 3 across the 6 of their papers we have counts for
6 papers
FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving
Henry Robbins, Connor Lawless, Madeleine Udell +1
Mixed-Integer Linear Programming (MILP) is a fundamental tool for combinatorial optimization with extensive real-world applications. A central challenge is designing computationall…
"It Was a Magical Box": Understanding Practitioner Workflows and Needs in Optimization
Connor Lawless, Jakob Schoeffer, Madeleine Udell
Optimization underpins decision-making in domains from healthcare to logistics, yet for many practitioners it remains a "magical box": powerful but opaque, difficult to use, and re…
Understanding Fixed Predictions via Confined Regions
Connor Lawless, Tsui-Wei Weng, Berk Ustun +1
Machine learning models can assign fixed predictions that preclude individuals from changing their outcome. Existing approaches to audit fixed predictions do so on a pointwise basi…
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations
Haotian Zhai, Connor Lawless, Ellen Vitercik +1
A fundamental problem in combinatorial optimization is identifying equivalent formulations. Despite the growing need for automated equivalence checks -- driven, for example, by opt…
LLMs for Cold-Start Cutting Plane Separator Configuration
Connor Lawless, Yingxi Li, Anders Wikum +2
Mixed integer linear programming (MILP) solvers expose hundreds of parameters that have an outsized impact on performance but are difficult to configure for all but expert users. E…
"I Want It That Way": Enabling Interactive Decision Support Using Large Language Models and Constraint Programming
Connor Lawless, Jakob Schoeffer, Lindy Le +5
A critical factor in the success of decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their prefe…