249 citations · 790 across the 59 of their papers we have counts for
69 papers
Intermittency for the parabolic Anderson model on spaces with sub-Gaussian heat kernel estimates
Fabrice Baudoin, Hongyi Chen, Li Chen +1
We study the parabolic Anderson model on an unbounded Ahlfors -regular Dirichlet metric measure space whose heat kernel satisfies two-sided sub-…
Predictive Dual Smoothing for Column Generation
Senne Berden, Noah Schutte, Andrea Lodi +1
Solving large-scale linear programs efficiently is an important challenge in many optimization settings. A key technique is column generation, which alternates between solving the…
An improved column-generation-based matheuristic for learning classification trees
Krunal Kishor Patel, Guy Desaulniers, Andrea Lodi
Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision trees are fast but gen…
Assortment Optimization with Visibility Constraints
Theo Barre, Omar El Housni, Marouane Ibn Brahim +2
Motivated by applications in e-retail and online advertising, we study the problem of assortment optimization under visibility constraints, that we refer to as APV. Here, we are gi…
Integer Programming Games: A Gentle Computational Overview
Margarida Carvalho, Gabriele Dragotto, Andrea Lodi +1
In this tutorial, we present a computational overview on computing Nash equilibria in Integer Programming Games (s), , how to compute solutions for a class of non-cooper…
Operational Research: Methods and Applications
Fotios Petropoulos, Gilbert Laporte, Emel Aktas +79
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This…
The Critical Node Game
Gabriele Dragotto, Amine Boukhtouta, Andrea Lodi +1
In this work, we introduce a game-theoretic model that assesses the cyber-security risk of cloud networks and informs security experts on the optimal security strategies. Our appro…
Recovering Dantzig-Wolfe Bounds by Cutting Planes
Rui Chen, Oktay Gunluk, Andrea Lodi
Dantzig-Wolfe (DW) decomposition is a well-known technique in mixed-integer programming (MIP) for decomposing and convexifying constraints to obtain potentially strong dual bounds.…
A machine learning framework for neighbor generation in metaheuristic search
Defeng Liu, Vincent Perreault, Alain Hertz +1
This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general mach…
A Framework for Fair Decision-making Over Time with Time-invariant Utilities
Andrea Lodi, Sriram Sankaranarayanan, Guanyi Wang
Fairness is a major concern in contemporary decision problems. In these situations, the objective is to maximize fairness while preserving the efficacy of the underlying decision-m…
Learning to repeatedly solve routing problems
Mouad Morabit, Guy Desaulniers, Andrea Lodi
In the last years, there has been a great interest in machine-learning-based heuristics for solving NP-hard combinatorial optimization problems. The developed methods have shown po…
Connectivity-constrained Interactive Panoptic Segmentation
Ruobing Shen, Bo Tang, Andrea Lodi +2
We address interactive panoptic annotation, where one segment all object and stuff regions in an image. We investigate two graph-based segmentation algorithms that both enforce con…
Online Decision Making with Fairness over Time
Rui Chen, Oktay Gunluk, Andrea Lodi +1
Online platforms increasingly rely on sequential decision-making algorithms to allocate resources, match users, or control exposure, while facing growing pressure to ensure fairnes…
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks
Abdel Ghani Labassi, Didier Chételat, Andrea Lodi
Branch-and-bound approaches in integer programming require ordering portions of the space to explore next, a problem known as node comparison. We propose a new siamese graph neural…
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?
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. In…
Explainable prediction of Qcodes for NOTAMs using column generation
Krunal Kishor Patel, Guy Desaulniers, Andrea Lodi +1
A NOtice To AirMen (NOTAM) contains important flight route related information. To search and filter them, NOTAMs are grouped into categories called QCodes. In this paper, we devel…
Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data
Igor Sadoune, Andrea Lodi, Marcelin Joanis
We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of au…
Deep Neural Networks pruning via the Structured Perspective Regularization
Matteo Cacciola, Antonio Frangioni, Xinlin Li +1
In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful tool, broadly used in many applications. Often, the selected (deep) architectures include many layers, an…
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization
Dounia Lakhmiri, Dominique Orban, Andrea Lodi
We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is only assumed to be lowe…
Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning
Eric Larsen, Emma Frejinger, Bernard Gendron +1
We propose a methodology at the nexus of operations research and machine learning (ML) leveraging generic approximators available from ML to accelerate the solution of mixed-intege…
Lookback for Learning to Branch
Prateek Gupta, Elias B. Khalil, Didier Chetélat +4
The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Progra…
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Elias B. Khalil, Christopher Morris, Andrea Lodi
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solve…
Capacity Variation in the Many-to-one Stable Matching
Federico Bobbio, Margarida Carvalho, Andrea Lodi +1
The many-to-one stable matching problem provides the fundamental abstraction of several real-world matching markets such as school choice and hospital-resident allocation. The agen…
Learning to branch with Tree MDPs
Lara Scavuzzo, Feng Yang Chen, Didier Chételat +4
State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule. The idea of learni…