Program for stream Machine Learning and Mathematical Optimization

MA-03: Optimal Classification and Regression Trees
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Cristina Molero-Río

• New optimization models for optimal classification trees
  Valentine Huré, Zacharie Ales, Amélie Lambert
• Randomized regression trees: a model variant with disaggregated predictions and a decomposition training algorithm
  Antonio Consolo, Edoardo Amaldi, Andrea Manno
• Learning Geospatial Decision Tree Splitters
  Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt
• On sparse optimal regression trees for multivariate functional data
  Cristina Molero-Río, Rafael Blanquero, Emilio CARRIZOSA, Dolores Romero Morales

MA-04: Machine Learning in Marketing and Behavioral Analytics (I)
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Vinicius Brei

• Utilitarian and hedonic consumer behavior in a supermarket setting: an unsupervised machine learning approach
  Burcin Bozkaya, Zeynep Kucuksari, Selim Balcisoy, Vinicius Brei
• A City Two Tales: NYC Neighborhood Resilience and Fragility to the COVID-19 Pandemic from a Mobility Network Perspective
  Selim Balcisoy, Mohsen Bahrami, Hasan Alp Boz, Aaron Nicholas, Nina Mazar, Burcin Bozkaya, Alex Pentland
• Adversarial risk analysis for competitive business decisions in uncertain environments
  Daniel Garcia Rasines, Simón Rodríguez Santana, Roi Naveiro, David Rios Insua
• Using satellite imagery and machine learning to forecast energy consumption
  Vilmar Boff, Vinicius Brei, Alina Flores, Carla Netto, Ricardo Limongi

MB-03: Machine Learning and Mathematical Optimization in Location and Logistics
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Cristina Molero-Río

• Predict-and-optimize to address uncertainty in logistics: a tailored neural network approach
  Nuria Gómez-Vargas, Emilio CARRIZOSA, Rafael Blanquero
• A Matheuristic for the Obnoxious p-Median Problem
  Tamara Bigler
• Operations Research to evaluate public transport performance in the EU
  Martina Fischetti, Davide Duma, Stefano Gualandi, Juan Nicolas Ibanez, Claudio Tomasi
• Learning to Solve Electric Vehicle Routing Problems with Nonlinear Charging Functions
  James Fitzpatrick, Deepak Ajwani, Paula Carroll

MB-04: Machine Learning in Marketing and Behavioral Analytics (II)
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Vinicius Brei

• Non-Parametric Assortment Optimization with Product-oriented Market Segmentation
  Milad Keshvari Fard
• Direct selling systems improvement through data science
  Julián E. Tornillo, Pablo B. Savian, Andrés Redchuk
• Adjusting a trained support vector machine in the light of new training data
  Seán McGarraghy, Milena Venkova
• Forecasting Customers Risk-Adjusted Revenue Using Topic Modelling Applications
  Marcos Machado, Salma Karray

MC-03: Supervised and unsupervised learning, and Mathematical Optimization
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Kseniia Kurishchenko

• Instance-dependent cost-sensitive learning for positive and unlabeled data in fraud detection
  Carlos Ortega Vazquez, Jochen De Weerdt, Seppe vanden Broucke
• Efficient and robust classification with Lipschitz Neural Networks
  Louis Béthune, Mathieu Serrurier
• Feature Selection, Dendrograms, and Minimum Spanning Trees
  Marina Leal Palazón, Martine Labbé, Mercedes Landete
• Side-constrained minimum sum-of-squares clustering: mathematical programming and random projections
  Benedetto Manca, Leo Liberti

MC-04: Machine learning for optimizing business decision-making I
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Wouter Verbeke, Kristof Coussement

• Probabilistic forecasting with modified N-BEATS networks
  Jente Van Belle, Ruben Crevits, Wouter Verbeke
• Deep Learning for Life Event Prediction in the Financial Industry
  Stephanie Beyer Diaz, Kristof Coussement, Arno De Caigny
• Leveraging uncertainty estimation for trustworthy predictions in decision-making
  Arthur Thuy, Dries Benoit
• Learning industry-sensitive language in business communication – Insights in BusinessBERT
  Philipp Borchert, Jochen De Weerdt, Kristof Coussement, Arno De Caigny

MD-03: Dealing with Fairness in Machine Learning through Mathematical Optimization
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Kseniia Kurishchenko

• Improving the fairness of Generalized Linear Models by feature shrinkage
  Marcela Galvis Restrepo, Dolores Romero Morales, Emilio CARRIZOSA
• On a mathematical optimization formulation to trade off accuracy and fairness in LASSO regression
  Thomas Halskov, Dolores Romero Morales, Emilio CARRIZOSA
• An integer optimization-based approach to fair clustering
  Philipp Baumann, Manuel Kammermann
• On fair random forests
  Kseniia Kurishchenko, Emilio CARRIZOSA, Dolores Romero Morales

MD-04: Machine learning for optimizing business decision-making II
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Wouter Verbeke, Kristof Coussement

• Investigating the beneficial impact of the logit leaf model for credit scoring
  Khaoula IDBENJRA, Kristof Coussement, Arno De Caigny
• Predicting Day-Ahead Stock Returns using Search Engine Query Volumes
  Christopher Bockel-Rickermann
• Predicting Credit Rating Migrations Combining Textual, Financial, and Market Data
  Manon Reusens, Kameswara Korangi, Seppe vanden Broucke, Christophe Mues, Cristian Bravo, Bart Baesens

TA-03: Counterfactual Explanations and Adversarial Learning
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Jasone Ramírez-Ayerbe

• Poisoning Hidden-Markov-Model Inferences on Batch Data
  Jose Manuel Camacho Rodriguez, William Caballero, Tahir Ekin, Roi Naveiro
• Scalable methods for solving games in Adversarial Machine Learning
  Roi Naveiro
• Model Extraction based on Counterfactual Explanations
  Veronica Piccialli, Cecilia Salvatore
• Counterfactual Explanations via Mathematical Optimization with applications to functional data
  Jasone Ramírez-Ayerbe, Emilio CARRIZOSA, Dolores Romero Morales

TA-04: Machine learning for optimizing business decision-making IV
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Wouter Verbeke, Kristof Coussement

• Failure prediction vs. maintenance prescription: optimizing maintenance interventions by learning individual treatment effects
  Toon Vanderschueren, Robert Boute, Tim Verdonck, Bart Baesens, Wouter Verbeke
• Uplift Modeling with High Class-Imbalance
  Otto Nyberg
• How counterfactual explanations can be used to detect bias in a machine learning model
  Sofie Goethals, David Martens
• Gaining insights into major public concerns during a crisis based on Twitter data
  Lisa Schetgen, Matthias Bogaert

TB-03: Machine Learning in Energy Systems
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Farzaneh Pourahmadi

• Strategies for Virtual Power Plant Bidding in Energy and Ancillary Service Markets
  Lesia Mitridati, Riccardo de Nardis di Prata
• Neural Networks for GNSS data Analysis, Positioning and Attitude Determination
  Raúl de Celis, Luis Cadarso
• Combining learning and optimization for real-time scheduling problems
  Farzaneh Pourahmadi

TB-04: Machine learning for optimizing business decision-making III
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Wouter Verbeke, Kristof Coussement

• Robust Instance-dependent Cost-sensitive Learning
  Simon De Vos, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, Wouter Verbeke
• Self-Supervised Anomaly Detection for Detecting Rogue Sensors in IoT Data
  Boje Deforce, Bart Baesens, Jan Diels, Estefanía Serral
• NICE: An Algorithm for Nearest Instance Counterfactual Explanations
  Dieter Brughmans, Pieter Leyman, David Martens

TC-03: Machine Learning and Mathematical Optimization in Banking and Finance
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Yujia Chen, Belen Martin Barragan

• Machine learning approaches to forecasting cryptocurrency volatility: considering internal and external determinants
  Yijun Wang, Galina Andreeva, Belen Martin Barragan
• Interpretable machine learning for imbalanced credit scoring datasets
  Yujia Chen, Belen Martin Barragan, Raffaella Calabrese
• Clustering-based optimization in fraud detection classifier training
  Dalia Breskuvienė, Gintautas Dzemyda
• Fairness of ML in the context of credit scoring
  Darie Moldovan

TC-04: Machine Learning and Mathematical Optimization: challenges and real-world applications
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): M. Asuncion Jimenez-Cordero

• Minimax Classification with 0-1 Loss and Performance Guarantees
  Santiago Mazuelas
• A Rule Generation Framework for Learning
  Tabea E. Röber, Ilker Birbil, M.Hakan AKYUZ
• Embedding machine learning models in the scheduling problem of pumped hydro energy storage
  Pietro Favaro, Jean-François Toubeau, François Vallée
• A novel machine-learning-aided approach for warm-starting constraint generation methods in MILPs
  M. Asuncion Jimenez-Cordero, Juan Miguel Morales, Salvador Pineda Morente

TD-03: Synergies: Learning for and with Optimization
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Gabriele Iommazzo, Andrea Lodi

• Deep Neural Networks pruning via the Structured Perspective Regularization
  Matteo Cacciola, Antonio Frangioni, Andrea Lodi
• Learning for Spatial Branching: An Algorithm Selection Approach
  Ignacio Gómez-Casares, Bissan Ghaddar, Julio González-Díaz, Brais González Rodríguez, Beatriz Pateiro-López, Sofía Rodríguez-Ballesteros
• Predict and Optimize: Through the Lens of Learning to Rank
  Jayanta Mandi, Victor Bucarey, Maxime Mulamba, Tias Guns

TD-04: Data driven decision making in OR
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Victor Bucarey

• Probability estimation and structured output prediction for learning preferences in last mile delivery
  Rocsildes Canoy, Victor Bucarey, Maxime Mulamba, Yves Molenbruch, Jayanta Mandi, Tias Guns
• A Support Vector approach to create smart rules as constraints for robust pricing optimization
  Luis Aburto
• Contrastive Losses and Solution Caching for Predict-and-Optimize
  Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti, Michele Lombardi, Victor Bucarey, Tias Guns
• Labor planning and shift scheduling in retail stores using customer traffic data
  Victor Bucarey

WA-03: Numerical methods in/for Machine Learning (I)
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Emilio CARRIZOSA

• On the convergence of controlled mini-batch gradient algorithms
  Laura Palagi, Giampaolo Liuzzi, Ruggiero Seccia
• Multi-objective simulation-based optimization: a neural network approach
  Marco Boresta, Tommaso Giovannelli, Stefano Lucidi, Massimo Roma
• DATA-DRIVEN SEARCH AND POPULATION MANAGEMENT IN A HEURISTIC
  Fulya Atalay, Necati Aras, Mustafa Baydoğan
• OptiCL: A Package for Mixed-Integer Optimization with Constraint Learning
  Ade Fajemisin

WA-04: Mathematical Optimization for Interpretable Supervised Learning
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Vanesa Guerrero

• Developing a relation between neural networks and polynomials with Taylor series and combinatorics: NN2Poly.
  Pablo Morala, Jenny Alexandra Cifuentes Quintero, Rosa Elvira Lillo Rodríguez, Iñaki Ucar
• A quantile based algorithm for partial least squares
  Álvaro Méndez, Rosa Elvira Lillo Rodríguez
• Feature selection on high dimensional additive models
  Manuel Navarro García, Vanesa Guerrero, Maria Durban
• On spline surrogate models and reformulation techniques for MINLPs with separable non-convexities
  Vanesa Guerrero, Claudia D'Ambrosio, Renan Spencer Trindade

WB-03: Numerical methods in/for Machine Learning (II)
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Adil Bagirov, Emilio CARRIZOSA

• Nonsmooth optimization algorithm for semisupervised clustering
  Adil Bagirov, Sona Taheri
• Optimization Approach Towards Improving Compactness and Separability of Clusters
  Sona Taheri, Adil Bagirov, Najmeh Hoseini Monjezi
• Machine Learning for the Per-Instance Configuration of MILP Solvers
  Daniel Schermer, Oliver Wendt
• Convex Support Vector Regression
  Zhiqiang Liao, Sheng Dai, Timo Kuosmanen

WB-04: Interpretable Machine Learning
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Connor Lawless

• Counterfactual Explanations with OptiCL
  Donato Maragno, Tabea E. Röber, Ilker Birbil
• Integer Programming for Causal Structure Learning in the Presence of Latent Variables
  Rui Chen, Sanjeeb Dash, Tian Gao
• Interpretable Bayesian classification under negative dependence with applications to wireless interference
  Sander Aarts
• Interpretable Clustering via Multi-Polytope Machines
  Connor Lawless

WC-03: Machine Learning, Mathematical Optimization and Health
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Emilio CARRIZOSA

• Comparative Analysis of Breast Cancer by Logical Analysis of Data (LAD) and Supervised ML Techniques
  Elnaz Gholipour, Béla Vizvári
• Feature selection problem on Breast cancer classification using an Improved Grey wolf optimizer
  Ms. Preeti, Kusum Deep
• Machine Learning in Brain-Computer Interface (BCI): Classifying EEG Signals obtained with Portable Technology
  Guadalupe Pascal, Andrés Redchuk
• Deep Learning Models for Pancreatic Cancer Detection in CT Images
  Olga Kurasova, Gintautas Dzemyda, Viktor Medvedev, Aušra Šubonienė, Rokas Gipiškis, Kęstutis Strupas, Aistė Gulla, Artūras Samuilis, Džiugas Jagminas

WC-04: Bayesian Statistical Learning methods
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): Pepa Ramirez Cobo

• Fairness in multiple linear regression: a Bayesian approach
  Rafael Jiménez-Llamas, Emilio CARRIZOSA, Pepa Ramirez Cobo
• Revisiting pitch framing using Bayesian Additive Regression Trees
  Sameer Deshpande
• Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models
  Lucas Kock, Nadja Klein, David J. Nott

WD-03: Mathematical Optimization, Machine Learning and Supply Chain
Stream: Machine Learning and Mathematical Optimization
Room: C (building Main lobby)
Chair(s): Emilio CARRIZOSA

• Cost patterns learning trough logistic and sorting models integration in Waste Management
  Diego Maria Pinto, Giuseppe Stecca, Marco Boresta
• The role of big data on bullwhip effect under rationing game: A control-theoretic approach
  Christos Papanagnou
• A Bilevel Optimization Approach for Feature Selection in the Data-Driven Newsvendor
  Breno Serrano, Stefan Minner, Maximilian Schiffer, Thibaut Vidal
• Derivative-free optimization in value chain optimization
  Damien van de Berg, Antonio del Rio Chanona, Nilay Shah

WD-04: The role of Statistics in Machine Learning algorithms
Stream: Machine Learning and Mathematical Optimization
Room: D (building Main lobby)
Chair(s): M. Remedios Sillero-Denamiel, Sandra Benítez-Peña

• Clustering in FDA applying machine learning and statistical techniques
  Belén Pulido Bravo, Alba M. Franco-Pereira, Rosa Elvira Lillo Rodríguez
• Estimating Class Probabilities in SVM
  Sandra Benítez-Peña, Rafael Blanquero, Emilio CARRIZOSA, Pepa Ramírez-Cobo
• EVALUATION OF THE TREATMENT ALTERNATIVES FOR SPINAL CORD TUMOR USING FUZZY-PROMETHEE
  Berna Uzun, Efe Precious Onakpojeruo, Ilker Ozsahin, Dılber Uzun Ozsahin
• Bayesian Regression for Selection Bias
  M. Remedios Sillero-Denamiel, Simon Wilson, Hieu Cao