Publications

Open-Set Multivariate Time-Series Anomaly Detection
Thomas Lai
Thi Kieu Khanh Ho
Narges Armanfard
2024-01-01
ECAI (publié)
doi.org
arxiv.org
Operational Research: methods and applications
Fotios Petropoulos
Gilbert Laporte
Emel Aktas
Sibel A. Alumur
Claudia Archetti
Hayriye Ayhan
Maria Battarra
Julia A. Bennell
Jean-Marie Bourjolly
John E. Boylan
Michèle Breton
David Canca
Laurent Charlin
Bo Chen
Cihan Tugrul Cicek
Louis Anthony Cox
Christine S.M. Currie
Erik Demeulemeester
Li Ding
Stephen M. Disney … (voir 62 de plus)
Matthias Ehrgott
Martin J. Eppler
Güneş Erdoğan
Bernard Fortz
L. Alberto Franco
Jens Frische
Salvatore Greco
Amanda J. Gregory
Raimo P. Hämäläinen
Willy Herroelen
Mike Hewitt
Jan Holmström
John N. Hooker
Tuğçe Işık
Jill Johnes
Bahar Y. Kara
Özlem Karsu
Katherine Kent
Charlotte Köhler
Martin Kunc
Yong-Hong Kuo
Judit Lienert
Adam N. Letchford
Janny Leung
Dong Li
Haitao Li
Ivana Ljubić
Andrea Lodi
Sebastián Lozano
Virginie Lurkin
Silvano Martello
Ian G. McHale
Gerald Midgley
John D.W. Morecroft
Akshay Mutha
Ceyda Oğuz
Sanja Petrovic
Ulrich Pferschy
Harilaos N. Psaraftis
Sam Rose
Lauri Saarinen
Said Salhi
Jing-Sheng Song
Dimitrios Sotiros
Kathryn E. Stecke
Arne K. Strauss
İstenç Tarhan
Clemens Thielen
Paolo Toth
Greet Vanden Berghe
Christos Vasilakis
Vikrant Vaze
Daniele Vigo
Kai Virtanen
Xun Wang
Rafał Weron
Leroy White
Tom Van Woensel
Mike Yearworth
E. Alper Yıldırım
Georges Zaccour
Xuying Zhao
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a … (voir plus)diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.
2024-01-01
J. Oper. Res. Soc. (publié)
doi.org
arxiv.org
Optimal Zero-Shot Detector for Multi-Armed Attacks
Federica Granese
Marco Romanelli
Pablo Piantanida
This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them va… (voir plus)rious avenues to introduce noise into the dataset. Our central objective is to protect the data by detecting any alterations to the input. We approach this defensive strategy with utmost caution, operating in an environment where the defender possesses significantly less information compared to the attacker. Specifically, the defender is unable to utilize any data samples for training a defense model or verifying the integrity of the channel. Instead, the defender relies exclusively on a set of pre-existing detectors readily available"off the shelf". To tackle this challenge, we derive an innovative information-theoretic defense approach that optimally aggregates the decisions made by these detectors, eliminating the need for any training data. We further explore a practical use-case scenario for empirical evaluation, where the attacker possesses a pre-trained classifier and launches well-known adversarial attacks against it. Our experiments highlight the effectiveness of our proposed solution, even in scenarios that deviate from the optimal setup.
2024-01-01
AISTATS (publié)
doi.org
arxiv.org
Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers
Umberto Cappellazzo
Daniele Falavigna
Alessio Brutti
Mirco Ravanelli
The common modus operandi of fine-tuning large pre-trained Transformer models entails the adaptation of all their parameters (i.e., full fin… (voir plus)e-tuning). While achieving striking results on multiple tasks, this approach becomes unfeasible as the model size and the number of downstream tasks increase. In natural language processing and computer vision, parameter-efficient approaches like prompt-tuning and adapters have emerged as solid alternatives by fine-tuning only a small number of extra parameters, without sacrificing performance accuracy. For audio classification tasks, the Audio Spectrogram Transformer model shows impressive results. However, surprisingly, how to efficiently adapt it to several downstream tasks has not been tackled before. In this paper, we bridge this gap and present a detailed investigation of common parameter-efficient methods, revealing that adapters and LoRA consistently outperform the other methods across four benchmarks. Whereas adapters prove to be more efficient in few-shot learning settings, LoRA turns out to scale better as we increase the number of learnable parameters. We finally carry out ablation studies to find the best configuration for adapters and LoRA.
2024-01-01
2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP) (publié)
doi.org
arxiv.org
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Alexandre AGM Duval
Victor Schmidt
Santiago Miret
Yoshua Bengio
Alex Hernandez-Garcia
David Rolnick
openreview.net
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Maxime Wabartha
Joelle Pineau
Learning inherently interpretable policies is a central challenge in the path to developing autonomous agents that humans can trust. We argu… (voir plus)e for the use of policies that are piecewise-linear. We carefully study to what extent they can retain the interpretable properties of linear policies while performing competitively with neural baselines. In particular, we propose the HyperCombinator (HC), a piecewise-linear neural architecture expressing a policy with a controllably small number of sub-policies. Each sub-policy is linear with respect to interpretable features, shedding light on the agent’s decision process without needing an additional explanation model. We evaluate HC policies in control and navigation experiments, visualize the improved interpretability of the agent and highlight its trade-off with performance.
2024-01-01
International Conference on Learning Representations (publié)
openreview.net
openreview.net
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Prakash Panangaden
Sahand Rezaei-Shoshtari
Rosie Zhao
David Meger
Doina Precup
arxiv.org
Population Monte Carlo With Normalizing Flow
Soumyasundar Pal
Antonios Valkanas
Mark Coates
Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inferen… (voir plus)ce of intractable distributions. Population Monte Carlo (PMC) algorithms constitute a family of AIS approaches which adapt the proposal distributions iteratively to improve the approximation of the target distribution. Recent work in this area primarily focuses on ameliorating the proposal adaptation procedure for high-dimensional applications. However, most of the AIS algorithms use simple proposal distributions for sampling, which might be inadequate in exploring target distributions with intricate geometries. In this work, we construct expressive proposal distributions in the AIS framework using normalizing flow, an appealing approach for modeling complex distributions. We use an iterative parameter update rule to enhance the approximation of the target distribution. Numerical experiments show that in high-dimensional settings, the proposed algorithm offers significantly improved performance compared to the existing techniques.
2024-01-01
IEEE Signal Processing Letters (publié)
doi.org
arxiv.org
Preserving Privacy in GANs Against Membership Inference Attack
Mohammadhadi Shateri
Francisco Messina
Fabrice Labeau
Pablo Piantanida
Generative Adversarial Networks (GANs) have been widely used for generating synthetic data for cases where there is a limited size real-worl… (voir plus)d data set or when data holders are unwilling to share their data samples. Recent works showed that GANs, due to overfitting and memorization, might leak information regarding their training data samples. This makes GANs vulnerable to Membership Inference Attacks (MIAs). Several defense strategies have been proposed in the literature to mitigate this privacy issue. Unfortunately, defense strategies based on differential privacy are proven to reduce extensively the quality of the synthetic data points. On the other hand, more recent frameworks such as PrivGAN and PAR-GAN are not suitable for small-size training data sets. In the present work, the overfitting in GANs is studied in terms of the discriminator, and a more general measure of overfitting based on the Bhattacharyya coefficient is defined. Then, inspired by Fano’s inequality, our first defense mechanism against MIAs is proposed. This framework, which requires only a simple modification in the loss function of GANs, is referred to as the maximum entropy GAN or MEGAN and significantly improves the robustness of GANs to MIAs. As a second defense strategy, a more heuristic model based on minimizing the information leaked from the generated samples about the training data points is presented. This approach is referred to as mutual information minimization GAN (MIMGAN) and uses a variational representation of the mutual information to minimize the information that a synthetic sample might leak about the whole training data set. Applying the proposed frameworks to some commonly used data sets against state-of-the-art MIAs reveals that the proposed methods can reduce the accuracy of the adversaries to the level of random guessing accuracy with a small reduction in the quality of the synthetic data samples.
2024-01-01
IEEE Transactions on Information Forensics and Security (publié)
doi.org
arxiv.org
Probabilistic Dataset Reconstruction from Interpretable Models
Julien Ferry
Ulrich Aivodji
Sébastien Gambs
Marie-José Huguet
Mohamed Siala
Interpretability is often pointed out as a key requirement for trustworthy machine learning. However, learning and releasing models that are… (voir plus) inherently interpretable leaks information regarding the underlying training data. As such disclosure may directly conflict with privacy, a precise quantification of the privacy impact of such breach is a fundamental problem. For instance, previous work have shown that the structure of a decision tree can be leveraged to build a probabilistic reconstruction of its training dataset, with the uncertainty of the reconstruction being a relevant metric for the information leak. In this paper, we propose of a novel framework generalizing these probabilistic reconstructions in the sense that it can handle other forms of interpretable models and more generic types of knowledge. In addition, we demonstrate that under realistic assumptions regarding the interpretable models' structure, the uncertainty of the reconstruction can be computed efficiently. Finally, we illustrate the applicability of our approach on both decision trees and rule lists, by comparing the theoretical information leak associated to either exact or heuristic learning algorithms. Our results suggest that optimal interpretable models are often more compact and leak less information regarding their training data than greedily-built ones, for a given accuracy level.
2024-01-01
SaTML (publié)
doi.org
openreview.net
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
Damien Ferbach
Baptiste Goujaud
Gauthier Gidel
Aymeric Dieuleveut
The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neura… (voir plus)l network architectures. Recent works have experimentally shown that two different solutions found after two runs of a stochastic training are often connected by very simple continuous paths (e.g., linear) modulo a permutation of the weights. In this paper, we provide a framework theoretically explaining this empirical observation. Based on convergence rates in Wasserstein distance of empirical measures, we show that, with high probability, two wide enough two-layer neural networks trained with stochastic gradient descent are linearly connected. Additionally, we express upper and lower bounds on the width of each layer of two deep neural networks with independent neuron weights to be linearly connected. Finally, we empirically demonstrate the validity of our approach by showing how the dimension of the support of the weight distribution of neurons, which dictates Wasserstein convergence rates is correlated with linear mode connectivity.
2024-01-01
AISTATS (publié)
doi.org
arxiv.org
Quantifying learning-style adaptation in effectiveness of LLM teaching
Ruben Weijers
Gabrielle Fidelis de Castilho
Jean-François Godbout
Reihaneh Rabbany
Kellin Pelrine
This preliminary study aims to investigate whether AI, when prompted based on individual learning styles, can effectively improve comprehens… (voir plus)ion and learning experiences in educational settings. It involves tailoring LLMs baseline prompts and comparing the results of a control group receiving standard content and an experimental group receiving learning style-tailored content. Preliminary results suggest that GPT-4 can generate responses aligned with various learning styles, indicating the potential for enhanced engagement and comprehension. However, these results also reveal challenges, including the model’s tendency for sycophantic behavior and variability in responses. Our findings suggest that a more sophisticated prompt engineering approach is required for integrating AI into education (AIEd) to improve educational outcomes.
2024-01-01
PERSONALIZE (publié)
www.semanticscholar.org