4 citations · 4 across the 4 of their papers we have counts for
5 papers
Preference Elicitation for Step-Wise Explanations in Logic Puzzles
Marco Foschini, Marianne Defresne, Emilio Gamba +2
Step-wise explanations can explain logic puzzles and other satisfaction problems by showing how to derive decisions step by step. Each step consists of a set of constraints that de…
Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
Jayanta Mandi, Marianne Defresne, Senne Berden +1
When some parameters of a constrained optimization problem (COP) are uncertain, this gives rise to a predict-then-optimize (PtO) problem, comprising two stages: the prediction of t…
Scaling Neuro-symbolic Problem Solving: Solver-Free Learning of Constraints and Objectives
Marianne Defresne, Romain Gambardella, Sophie Barbe +1
In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or…
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation
Marianne Defresne, Jayanta Mandi, Tias Guns
Real-life combinatorial optimization problems often involve several conflicting objectives, such as price, product quality and sustainability. A computationally-efficient way to ta…
Scalable Coupling of Deep Learning with Logical Reasoning
Marianne Defresne, Sophie Barbe, Thomas Schiex
In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or…