1 code implementation • 29 Feb 2024 • Julien Ferry, Ricardo Fukasawa, Timothée Pascal, Thibaut Vidal
Even with bootstrap aggregation, the majority of the data can also be reconstructed.
no code implementations • 10 Feb 2024 • Breno Serrano, Alexandre M. Florio, Stefan Minner, Maximilian Schiffer, Thibaut Vidal
We study the vehicle routing problem with time windows (VRPTW) and stochastic travel times, in which the decision-maker observes related contextual information, represented as feature variables, before making routing decisions.
no code implementations • 17 Jun 2023 • Utsav Sadana, Abhilash Chenreddy, Erick Delage, Alexandre Forel, Emma Frejinger, Thibaut Vidal
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty.
1 code implementation • 8 Feb 2023 • Kai Jungel, Axel Parmentier, Maximilian Schiffer, Thibaut Vidal
Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution.
1 code implementation • 5 Feb 2023 • Raphael Araujo Sampaio, Joaquim Dias Garcia, Marcus Poggi, Thibaut Vidal
We develop more effective optimization algorithms for general GMMs, and we combine these algorithms with regularization strategies that avoid overfitting.
1 code implementation • 24 Jan 2023 • Alexandre Forel, Axel Parmentier, Thibaut Vidal
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters.
no code implementations • 12 Sep 2022 • Breno Serrano, Stefan Minner, Maximilian Schiffer, Thibaut Vidal
The lower-level problem learns the optimal coefficients of the decision function on a training set, using only the features selected by the upper-level.
no code implementations • 9 Sep 2022 • Ítalo Santana, Andrea Lodi, Thibaut Vidal
Extensive research has been conducted, over recent years, on various ways of enhancing heuristic search for combinatorial optimization problems with machine learning algorithms.
1 code implementation • 15 Jul 2022 • Ítalo Santana, Breno Serrano, Maximilian Schiffer, Thibaut Vidal
The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function.
no code implementations • 28 May 2022 • Alexandre M. Florio, Pedro Martins, Maximilian Schiffer, Thiago Serra, Thibaut Vidal
Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth.
1 code implementation • 27 May 2022 • Alexandre Forel, Axel Parmentier, Thibaut Vidal
Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier.
1 code implementation • 11 Jun 2021 • Axel Parmentier, Thibaut Vidal
Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions.
1 code implementation • 5 Apr 2021 • Daniel Gribel, Michel Gendreau, Thibaut Vidal
Pairwise relational information is a useful way of providing partial supervision in domains where class labels are difficult to acquire.
1 code implementation • 26 Jan 2021 • Breno Serrano, Thibaut Vidal
The standard approach of community detection based on the DCSBM is to search for the model parameters that are the most likely to have produced the observed network data through maximum likelihood estimation (MLE).
2 code implementations • 23 Nov 2020 • Thibaut Vidal
The vehicle routing problem is one of the most studied combinatorial optimization topics, due to its practical importance and methodological interest.
1 code implementation • 21 Apr 2020 • Daniel Gribel, Thibaut Vidal, Michel Gendreau
Stochastic block models (SBMs) are often used to find assortative community structures in networks, such that the probability of connections within communities is higher than in between communities.
1 code implementation • ICML 2020 • Thibaut Vidal, Toni Pacheco, Maximilian Schiffer
The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives.
1 code implementation • 24 Dec 2019 • Florian Arnold, Ítalo Santana, Kenneth Sörensen, Thibaut Vidal
During the local search, each pattern is used to define one move in which 1) incompatible edges are disconnected, 2) the edges defined by the pattern are reconnected, and 3) the remaining solution fragments are optimally reconnected.
1 code implementation • 10 Oct 2019 • Jordana Mecler, Anand Subramanian, Thibaut Vidal
The job sequencing and tool switching problem (SSP) has been extensively studied in the field of operations research, due to its practical relevance and methodological interest.
no code implementations • 16 Jun 2019 • Thibaut Vidal, Gilbert Laporte, Piotr Matl
The diversity of applications has motivated the study of a myriad of problem variants with different attributes.
no code implementations • 28 Aug 2018 • Mayra Albuquerque, Thibaut Vidal
A minimum dominating set in a graph is a minimum set of vertices such that every vertex of the graph either belongs to it, or is adjacent to one vertex of this set.
1 code implementation • 25 Apr 2018 • Daniel Gribel, Thibaut Vidal
This may be related to differences of computational effort, or to the assumption that a near-optimal solution of the MSSC has only a marginal impact on clustering validity.
no code implementations • 16 Mar 2018 • Túlio A. M. Toffolo, Thibaut Vidal, Tony Wauters
We investigate a structural decomposition for the capacitated vehicle routing problem (CVRP) based on vehicle-to-customer "assignment" and visits "sequencing" decision variables.
no code implementations • 18 Feb 2018 • Matheus Nohra Haddad, Rafael Martinelli, Thibaut Vidal, Luiz Satoru Ochi, Simone Martins, Marcone Jamilson Freitas Souza, Richard Hartl
The core of the metaheuristic consists of a new large neighborhood search, which reduces the problem of finding the best insertion combination of a pickup and delivery pair into a route (with possible splits) to a resource-constrained shortest path and knapsack problem.
1 code implementation • 11 Aug 2015 • Thibaut Vidal
In the vehicle routing literature, Split is usually assimilated to the search for a shortest path in a directed acyclic graph $\mathcal{G}$ and solved in $O(nB)$ using Bellman's algorithm, where $n$ is the number of delivery points and $B$ is the average number of feasible routes that start with a given customer in the giant tour.
Data Structures and Algorithms
no code implementations • 26 Apr 2014 • Thibaut Vidal, Maria Battarra, Anand Subramanian, Güneş Erdoǧan
The second algorithm is a Hybrid Genetic Search, for which the shortest Hamiltonian path between each pair of vertices within each cluster should be precomputed.