1 citations · 3 across the 5 of their papers we have counts for
5 papers
Top-Down Knowledge Compilation for Counting Modulo Theories
Vincent Derkinderen, Pedro Zuidberg Dos Martires, Samuel Kolb +1
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF). Translating an arbitrary formul…
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…
Human-Machine Collaboration for Democratizing Data Science
Clément Gautrais, Yann Dauxais, Stefano Teso +3
Everybody wants to analyse their data, but only few posses the data science expertise to to this. Motivated by this observation we introduce a novel framework and system \textsc{Vi…
Monte Carlo Anti-Differentiation for Approximate Weighted Model Integration
Pedro Zuidberg Dos Martires, Samuel Kolb
Probabilistic inference in the hybrid domain, i.e. inference over discrete-continuous domains, requires tackling two well known #P-hard problems 1)~weighted model counting (WMC) ov…