Accelerated Benders Decomposition and Local Branching for Dynamic Maximum Covering Location Problems
Steven Lamontagne
Ribal Atallah
The maximum covering location problem (MCLP) is a key problem in facility location, with many applications and variants. One such variant is… (voir plus) the dynamic (or multi-period) MCLP, which considers the installation of facilities across multiple time periods. To the best of our knowledge, no exact solution method has been proposed to tackle large-scale instances of this problem. To that end, in this work, we expand upon the current state-of-the-art branch-and-Benders-cut solution method in the static case, by exploring several acceleration techniques. Additionally, we propose a specialised local branching scheme, that uses a novel distance metric in its definition of subproblems and features a new method for efficient and exact solving of the subproblems. These methods are then compared through extensive computational experiments, highlighting the strengths of the proposed methodologies.
A logistics provider’s profit maximization facility location problem with random utility maximizing followers
David Pinzon Ulloa
Bernard Gendron
GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon
Giuliano Antoniol
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Jerry Huang
Razvan Pascanu
Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models. The strength of su… (voir plus)ch optimizers is that they exhibit fast convergence while being more robust to hyperparameter choice. However, they often generalize worse than non-adaptive methods. Recent studies have tied this performance gap to flat minima selection: adaptive methods tend to find solutions in sharper basins of the loss landscape, which in turn hurts generalization. To overcome this issue, we propose a new memory-augmented version of Adam that promotes exploration towards flatter minima by using a buffer of critical momentum terms during training. Intuitively, the use of the buffer makes the optimizer overshoot outside the basin of attraction if it is not wide enough. We empirically show that our method improves the performance of several variants of Adam on standard supervised language modelling and image classification tasks.
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Nima Fathi
Amar Kumar
Brennan Nichyporuk
Mohammad Havaei
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (voir plus)with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at
Implementation of a Global Pediatric Trauma Course in an Upper Middle–Income Country: A Pilot Study
Abbie Naus
Madeleine Carroll
Ayla Gerk
David P. Mooney
Natalie L. Yanchar
Julia Ferreira
Karen E. Gripp
Caroline Ouellet
Fabio Botelho
On the Costs and Benefits of Adopting Lifelong Learning for Software Analytics -- Empirical Study on Brown Build and Risk Prediction
Doriane Olewicki
Sarra Habchi
Mathieu Nayrolles
Mojtaba Faramarzi
Bram Adams
Nowadays, software analytics tools using machine learning (ML) models to, for example, predict the risk of a code change are well establishe… (voir plus)d. However, as the goals of a project shift over time, and developers and their habits change, the performance of said models tends to degrade (drift) over time. Current retraining practices typically require retraining a new model from scratch on a large updated dataset when performance decay is observed, thus incurring a computational cost; also there is no continuity between the models as the past model is discarded and ignored during the new model training. Even though the literature has taken interest in online learning approaches, those have rarely been integrated and evaluated in industrial environments. This paper evaluates the use of lifelong learning (LL) for industrial use cases at Ubisoft, evaluating both the performance and the required computational effort in comparison to the retraining-from-scratch approaches commonly used by the industry. LL is used to continuously build and maintain ML-based software analytics tools using an incremental learner that progressively updates the old model using new data. To avoid so-called"catastrophic forgetting"of important older data points, we adopt a replay buffer of older data, which still allows us to drastically reduce the size of the overall training dataset, and hence model training time.
Structured Learning in Time-dependent Cox Models
Guanbo Wang
Yi Lian
Robert W. Platt
Rui Wang
Sylvie Perreault
Marc Dorais
Mireille E. Schnitzer
Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning.
Mariangela Panniello
Colleen J Gillon
Roberto Maffulli
Marco Celotto
Stefano Panzeri
Michael M Kohl
Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches
Léo Boisvert
Hélène Verhaeghe
Managing AI Risks in an Era of Rapid Progress
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
Anca Dragan … (voir 5 de plus)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
Assortment Optimization with Visibility Constraints
Théo Barré
Omar El Housni