Publications

Solving Combinatorial Pricing Problems using Embedded Dynamic Programming Models
Quang Minh Bui
Margarida Carvalho
José Neto
The combinatorial pricing problem (CPP) is a bilevel problem in which the leader maximizes their revenue by imposing tolls on certain items … (see more)that they can control. Based on the tolls set by the leader, the follower selects a subset of items corresponding to an optimal solution of a combinatorial optimization problem. To accomplish the leader's goal, the tolls need to be sufficiently low to discourage the follower from choosing the items offered by the competitors. In this paper, we derive a single-level reformulation for the CPP by rewriting the follower's problem as a longest path problem using a dynamic programming model, and then taking its dual and applying strong duality. We proceed to solve the reformulation in a dynamic fashion with a cutting plane method. We apply this methodology to 2 distinct dynamic programming models, namely, a novel formulation designated as selection diagram and the well-known decision diagram. We also produce numerical results to evaluate their performances across 3 different specializations of the CPP and a closely related problem that is the knapsack interdiction problem. Our results showcase the potential of the 2 proposed reformulations over the natural value function approach, expanding the set of tools to solve combinatorial bilevel programs.
2025-04-28
INFORMS Journal on Computing (published)
doi.org
arxiv.org
How Programmers Interact with Multimodal Software Documentation
Deeksha M. Arya
Jin Guo
Martin P. Robillard
There is a wide variety of online documentation to learn about a given software technology, and prior research has reported that programmers… (see more) must invest time and effort to identify one that best suits their need. We evaluated five modalities to present information that enable a software document to cater to the different presentation needs of programmers. We developed a prototype tutorial with these modalities on three topics in Java, namely, regular expressions, inheritance, and exception handling. We investigated how people interact with the modalities in the tutorial given a programming topic and a type of task. We conducted a survey study with 56 respondents and confirm that although text content is most useful for solving conceptual tasks, code examples support deeper comprehension of the underlying concepts. Furthermore, we report that respondents' contradicting preferences for the modalities suggest the need to have multiple alternatives in a software tutorial.
2025-04-27
2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) (published)
doi.org
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
Mingqi Yuan
Roger Creus Castanyer
Bin Li
Xin Jin
Glen Berseth
Wenjun Zeng
2025-04-24
TMLR (accepted)
doi.org
openreview.net
What makes a good public EV charging station? A revealed preference study
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Ribal Atallah
To determine the optimal locations for electric vehicle charging stations, optimisation models need to predict which charging stations users… (see more) will select. We estimate discrete choice models to predict the usage of charging stations using only readily available information for charging network operators. Our parameter values are estimated from a unique, revealed preferences dataset of charging sessions in Montreal, Quebec. We find that user distance to stations, proximity to home areas, and the number of outlets at each station are significant factors for predicting station usage. Additionally, amenities near charging stations have a neutral effect overall, with some users demonstrating strong preference or aversion for these locations. High variability among the preferences of users highlight the importance of models which incorporate panel effects. Moreover, integrating mixed logit models within the optimization of charging station network design yields high-quality solutions, even when evaluated under other model specifications.
2025-04-24
ArXiv (preprint)
arxiv.org
arxiv.org
What makes a good public EV charging station? A revealed preference study
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Ribal Atallah
2025-04-24
ArXiv (preprint)
arxiv.org
arxiv.org
Algorithmic Fairness Through the Lens of Metrics and Evaluation (AFME) 2024
Miriam Rateike
Awa Dieng
Jamelle Watson-Daniels
Ferdinando Fioretto
Golnoosh Farnadi
2025-04-23
Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (published)
proceedings.mlr.press
Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML
Prakhar Ganeesh
Usman Gohar
Lu Cheng
Golnoosh Farnadi
With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often … (see more)compared against each other to find the best method. These benchmarking efforts tend to use a common setup for evaluation under the assumption that providing a uniform environment ensures a fair comparison. However, bias mitigation techniques are sensitive to hyperparameter choices, random seeds, feature selection, etc., meaning that comparison on just one setting can unfairly favour certain algorithms. In this work, we show significant variance in fairness achieved by several algorithms and the influence of the learning pipeline on fairness scores. We highlight that most bias mitigation techniques can achieve comparable performance, given the freedom to perform hyperparameter optimization, suggesting that the choice of the evaluation parameters-rather than the mitigation technique itself-can sometimes create the perceived superiority of one method over another. We hope our work encourages future research on how various choices in the lifecycle of developing an algorithm impact fairness, and trends that guide the selection of appropriate algorithms.
2025-04-23
Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (published)
doi.org
arxiv.org
Distilling semantically aware orders for autoregressive image generation
Rishav Pramanik
Antoine Poupon
Juan A. Rodriguez
Masih Aminbeidokhti
David Vazquez
Chris Pal
Zhaozheng Yin
Marco Pedersoli
2025-04-23
ArXiv (preprint)
arxiv.org
arxiv.org
Distilling semantically aware orders for autoregressive image generation
Rishav Pramanik
Antoine Poupon
Juan A. Rodriguez
Masih Aminbeidokhti
David Vazquez
Chris Pal
Zhaozheng Yin
Marco Pedersoli
2025-04-23
ArXiv (preprint)
doi.org
arxiv.org
Fair Resource Allocation in Weakly Coupled Markov Decision Processes
Xiaohui Tu
Yossiri Adulyasak
Nima Akbarzadeh
Erick Delage
We consider fair resource allocation in sequential decision-making environments modeled as weakly coupled Markov decision processes, where r… (see more)esource constraints couple the action spaces of
2025-04-23
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (published)
proceedings.mlr.press
openreview.net
Feasible Learning
Juan Ramirez
Ignacio Hounie
Juan Elenter
Jose Gallego-Posada
Meraj Hashemizadeh
Alejandro Ribeiro
Simon Lacoste-Julien
We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bound… (see more)s the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory performance \emph{on every individual data point}. Since any model that meets the prescribed performance threshold is a valid FL solution, the choice of optimization algorithm and its dynamics play a crucial role in shaping the properties of the resulting solutions. In particular, we study a primal-dual approach which dynamically re-weights the importance of each sample during training. To address the challenge of setting a meaningful threshold in practice, we introduce a relaxation of FL that incorporates slack variables of minimal norm. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.
2025-04-23
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (published)
proceedings.mlr.press
A flaw in using pre-trained pLLMs in protein-protein interaction inference models
Joseph Szymborski
Amin Emad
With the growing pervasiveness of pre-trained protein large language models (pLLMs), pLLM-based methods are increasingly being put forward f… (see more)or the protein-protein interaction (PPI) inference task. Here, we identify and confirm that existing pre-trained pLLMs are a source of data leakage for the downstream PPI task. We characterize the extent of the data leakage problem by training and comparing small and efficient pLLMs on a dataset that controls for data leakage (“strict”) with one that does not (“non-strict”). While data leakage from pre-trained pLLMs cause measurable inflation of testing scores, we find that this does not necessarily extend to other, non-paired biological tasks such as protein keyword annotation. Further, we find no connection between the context-lengths of pLLMs and the performance of pLLM-based PPI inference methods on proteins with sequence lengths that surpass it. Furthermore, we show that pLLM-based and non-pLLM-based models fail to generalize in tasks such as prediction of the human-SARS-CoV-2 PPIs or the effect of point mutations on binding-affinities. This study demonstrates the importance of extending existing protocols for the evaluation of pLLM-based models applied to paired biological datasets and identifies areas of weakness of current pLLM models.
2025-04-23
bioRxiv (preprint)
doi.org