Search Results for author:
Found 54 papers, 23 papers with code
One-for-many Counterfactual Explanations by Column Generation
In this paper, we consider the problem of generating a set of counterfactual explanations for a group of instances, with the one-for-many allocation rule, where one explanation is allocated to a subgroup of the instances.
Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
We also address how to represent MILPs in the context of applying learning algorithms, MILP benchmarks and software.
A Reinforcement Learning Approach for Dynamic Rebalancing in Bike-Sharing System
Our work offers practical insights for operators and enriches the integration of reinforcement learning into dynamic rebalancing problems, paving the way for more intelligent and robust urban mobility solutions.
An improved column-generation-based matheuristic for learning classification trees
This approach improves scalability and can work with large datasets.
Structured Pruning of Neural Networks for Constraints Learning
In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse applications, including cancer treatment, algorithmic configuration, and chemical process optimization.
A machine learning framework for neighbor generation in metaheuristic search
Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space.
Learning to repeatedly solve routing problems
This partial prediction of the solution reduces the complexity of the problem and speeds up its resolution, while yielding a good quality solution.
Connectivity-constrained Interactive Panoptic Segmentation
We address interactive panoptic annotation, where one segment all object and stuff regions in an image.
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks
Branch-and-bound approaches in integer programming require ordering portions of the space to explore next, a problem known as node comparison.
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?
Extensive research has been conducted, over recent years, on various ways of enhancing heuristic search for combinatorial optimization problems with machine learning algorithms.
Explainable prediction of Qcodes for NOTAMs using column generation
A NOtice To AirMen (NOTAM) contains important flight route related information.
Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data
We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data.
Lookback for Learning to Branch
Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure.
Deep Neural Networks pruning via the Structured Perspective Regularization
In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful tool, broadly used in many applications.
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization
We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure.
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems.
Learning to branch with Tree MDPs
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.
Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning
Our extensive empirical analysis is grounded in standardized families of problems derived from stochastic server location (SSLP) and stochastic multi knapsack (SMKP) problems available in the literature.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
Machine-learning-based arc selection for constrained shortest path problems in column generation
Column generation is an iterative method used to solve a variety of optimization problems.
Revisiting local branching with a machine learning lens
In this work, we study the relation between the size of the search neighborhood and the behavior of the underlying LB algorithm, and we devise a leaning based framework for predicting the best size for the specific instance to be solved.
Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning
In this paper, we consider both first- and second-order techniques to address continuous optimization problems arising in machine learning.
Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems
Despite the extensive research efforts and the remarkable results obtained on Vehicle Routing Problems (VRP) by using algorithms proposed by the Machine Learning community that are partially or entirely based on data-driven analysis, most of these approaches are still seldom employed by the Operations Research (OR) community.
On the estimation of discrete choice models to capture irrational customer behaviors
Specifically, we show how to use partially-ranked preferences to efficiently model rational and irrational customer types from transaction data.
Learning to Schedule Heuristics in Branch and Bound
Compared to the default settings of a state-of-the-art academic MIP solver, we are able to reduce the average primal integral by up to 49% on two classes of challenging instances.
Ecole: A Library for Learning Inside MILP Solvers
In this paper we describe Ecole (Extensible Combinatorial Optimization Learning Environments), a library to facilitate integration of machine learning in combinatorial optimization solvers.
Learning to Schedule Heuristics in Branch-and-Bound
In this work, we propose the first data-driven framework for scheduling heuristics in an exact MIP solver.
Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc clouds
Drones have been getting more and more popular in many economy sectors.
Exploring the Wasserstein metric for survival analysis
Survival analysis is a type of semi-supervised task where the target output (the survival time) is often right-censored.
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer science.
Predicting the probability distribution of bus travel time to move towards reliable planning of public transport services
Two types of probabilistic models, namely similarity-based density estimation models and a smoothed Logistic Regression for probabilistic classification model, are compared on a dataset of more than 41, 000 trips and 50 bus routes of the city of Montr\'eal.
Reinforcement Learning for Freight Booking Control Problems
Routinely solving such operational problems when deploying reinforcement learning algorithms may be too time consuming.
Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?
This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keeping the system as before.
Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation
We propose a new stochastic variance-reduced damped L-BFGS algorithm, where we leverage estimates of bounds on the largest and smallest eigenvalues of the Hessian approximation to balance its quality and conditioning.
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
We present Ecole, a new library to simplify machine learning research for combinatorial optimization.
Change Point Detection by Cross-Entropy Maximization
Many offline unsupervised change point detection algorithms rely on minimizing a penalized sum of segment-wise costs.
Hybrid Models for Learning to Branch
First, in a more realistic setting where only a CPU is available, is the GNN model still competitive?
The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case
Besides automating field operations, a drone swarm can serve as an ad-hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other connected elements.
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
We aim instead at learning a policy that generalizes across heterogeneous MILPs: our main hypothesis is that parameterizing the state of the B&B search tree can aid this type of generalization.
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages.
JANOS: An Integrated Predictive and Prescriptive Modeling Framework
The framework considers two sets of decision variables; regular and predicted.
Game theoretical analysis of Kidney Exchange Programs
Recently, this was formulated as a non-cooperative two-player game and the game solutions (equilibria) were characterized when the entities objective function is the number of their patients receiving a kidney.
Computer Science and Game Theory 91-XX, 05Cxx, 90-XX
Learning chordal extensions
As a first building block of the learning framework, we propose an on-policy imitation learning scheme that mimics the elimination ordering provided by the (classical) minimum degree rule.
When Nash Meets Stackelberg
This article introduces a class of $Nash$ games among $Stackelberg$ players ($NASPs$), namely, a class of simultaneous non-cooperative games where the players solve sequential Stackelberg games.
Computer Science and Game Theory Optimization and Control
Connectivity-constrained interactive annotations for panoptic segmentation
Large-scale ground truth data sets are of crucial importance for deep learning based segmentation models, but annotating per-pixel masks is prohibitively time consuming.
Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location
Although presented through the application to the facility location problem, the approach developed here is general and explores a new perspective on the exploitation of past experience in combinatorial optimization.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.
Activation Adaptation in Neural Networks
Given the activation function, the neural network is trained over the bias and the weight parameters.
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm.
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems.
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
We aim to predict at a high speed the expected TDOS associated with the second stage problem, conditionally on the first stage variables.
Learning to rank for censored survival data
Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored.
An ILP Solver for Multi-label MRFs with Connectivity Constraints
The method enforces connectivity priors iteratively by a cutting plane method, and provides feasible solutions with a guarantee on sub-optimality even if we terminate it earlier.
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients.
