Search Results for author: Samuel Kolb

Found 5 papers, 0 papers with code

Top-Down Knowledge Compilation for Counting Modulo Theories

no code implementations7 Jun 2023 Vincent Derkinderen, Pedro Zuidberg Dos Martires, Samuel Kolb, Paolo Morettin

Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF).

Negation

Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation

no code implementations8 Feb 2022 Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt

Combinatorial optimisation problems are ubiquitous in artificial intelligence.

Learning Mixed-Integer Linear Programs from Contextual Examples

no code implementations15 Jul 2021 Mohit Kumar, Samuel Kolb, Luc De Raedt, Stefano Teso

In this paper, we study the problem of acquiring MILPs from contextual examples, a novel and realistic setting in which examples capture solutions and non-solutions within a specific context.

Scheduling

Human-Machine Collaboration for Democratizing Data Science

no code implementations23 Apr 2020 Clément Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt

Everybody wants to analyse their data, but only few posses the data science expertise to to this.

Clustering

Monte Carlo Anti-Differentiation for Approximate Weighted Model Integration

no code implementations13 Jan 2020 Pedro Zuidberg Dos Martires, Samuel Kolb

For both of these problems inference techniques have been developed separately in order to manage their #P-hardness, such as knowledge compilation for WMC and Monte Carlo (MC) methods for (approximate) integration in the continuous domain.

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