6 citations · 10 across the 5 of their papers we have counts for
4 papers · 1 filter
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
Mohit Kumar, Samuel Kolb, Stefano Teso +1
Combinatorial optimisation problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor…
Learning Mixed-Integer Linear Programs from Contextual Examples
Mohit Kumar, Samuel Kolb, Luc De Raedt +1
Mixed-integer linear programs (MILPs) are widely used in artificial intelligence and operations research to model complex decision problems like scheduling and routing. Designing s…
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning
Teodora Popordanoska, Mohit Kumar, Stefano Teso
Recent work has demonstrated the promise of combining local explanations with active learning for understanding and supervising black-box models. Here we show that, under specific…
Automating Personnel Rostering by Learning Constraints Using Tensors
Mohit Kumar, Stefano Teso, Luc De Raedt
Many problems in operations research require that constraints be specified in the model. Determining the right constraints is a hard and laborsome task. We propose an approach to a…