Publications

Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients
Mete Kemertas
Allan Jepson
Amir-massoud Farahmand
2024-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
arxiv.org
Embedding Cultural Diversity in Prototype-based Recommender Systems
Armin Moradi
Nicola Neophytou
Florian Carichon
Golnoosh Farnadi
Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing u… (voir plus)nderrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic biases within prototype-based matrix factorization methods. Using the country of origin as a proxy for cultural identity, we link this demographic attribute to popularity bias by refining the embedding space learning process. First, we propose filtering out irrelevant prototypes to improve representativity. Second, we introduce a regularization technique to enforce a uniform distribution of prototypes within the embedding space. Across four datasets, our results demonstrate a 27\% reduction in the average rank of long-tail items and a 2\% reduction in the average rank of items from underrepresented countries. Additionally, our model achieves a 2\% improvement in HitRatio@10 compared to the state-of-the-art, highlighting that fairness is enhanced without compromising recommendation quality. Moreover, the distribution of prototypes leads to more inclusive explanations by better aligning items with diverse prototypes.
2024-12-31
European Conference on Information Retrieval (publié)
doi.org
arxiv.org
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
Hiroki Naganuma
Ryuichiro Hataya
Kotaro Yoshida
Ioannis Mitliagkas
2024-12-31
Trans. Mach. Learn. Res. (publié)
openreview.net
openreview.net
EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data
Rohan Banerjee
Merve Kaptan
Alexandra Tinnermann
Ali Khatibi
Alice Dabbagh
Christian Büchel
Christian W. Kündig
Christine S.W. Law
Christine S.W. Law
Dario Pfyffer
David J. Lythgoe
Dimitra Tsivaka
Dimitri Van De Ville
Falk Eippert
Fauziyya Muhammad
Gary H. Glover
Gergely David
Grace Haynes
Jan Haakers
Jonathan C.W. Brooks … (voir 23 de plus)
Jürgen Finsterbusch
Katherine T. Martucci
Kimberly J. Hemmerling
Mahdi Mobarak-Abadi
Mark A. Hoggarth
Matthew A. Howard
Molly G. Bright
Nawal Kinany
Olivia S. Kowalczyk
Patrick Freund
Robert L. Barry
Sean Mackey
Shahabeddin Vahdat
Simon Schading
Stephen B. McMahon
Todd Parish
Veronique Marchand-Pauvert
Yufen Chen
Zachary A. Smith
Kenneth A. Weber II
Kenneth A. Weber II
Benjamin De Leener
Julien Cohen-Adad
Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Prepro… (voir plus)cessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.1 and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared with other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.
2024-12-31
Imaging Neuroscience (publié)
doi.org
Estimating Head Motion in Structural MRI Using a Deep Neural Network Trained on Synthetic Artifacts
C Bricout
S Ebrahimi Kahou
Sylvain Bouix
2024-12-31
arXiv.org (prépublication)
doi.org
On Estimating the Strength of Differentially Private Mechanisms in a Black-Box Setting
Daniele Gorla
Louis Jalouzot
Federica Granese
Catuscia Palamidessi
Pablo Piantanida
We analyze to what extent final users can infer information about the level of protection of their data when the data obfuscation mechanism … (voir plus)is a priori unknown to them (the so-called “black-box” scenario). In particular, we explore four notions of differential privacy, namely local/central
2024-12-31
IEEE Transactions on Dependable and Secure Computing (inconnu)
doi.org
Evaluating machine learning-driven intrusion detection systems in IoT: Performance and energy consumption
Saeid Jamshidi
Kawser Wazed Nafi
Amin Nikanjam
Foutse Khomh
2024-12-31
Comput. Ind. Eng. (publié)
doi.org
arxiv.org
Evaluation of machine learning and deep learning models for the classification of a single extracellular vesicles spectral library
C. del Real Mata
Y. Lu
M. Jalali
A. Bocan
M. Khatami
L. Montermini
J. McCormack-llersich
W. W. Reisner
L. Garzia
J. Rak
D. Bzdok
S. Mahshid
Nanostructure-based sensors study extracellular vesicles; optimization of a single-vesicle resolution spectral library to enhance classifica… (voir plus)tion for future AI-driven diagnostics.
2024-12-31
Sensors and Diagnostics (publié)
doi.org
Evolution of High-Throughput Satellite Systems: A Vision of Programmable Regenerative Payload.
Olfa Ben Yahia
Zineb Garroussi
Olivier Bélanger
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
Antoine Lesage-Landry
Gunes Karabulut Kurt
High-throughput satellite (HTS), with its digital payload technology, is expected to play a key role as an enabler of the upcoming sixth-gen… (voir plus)eration (6G) networks. HTS is mainly designed to provide higher data rates and capacities. Fueled by technological advancements, including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS has emerged as a fundamental component for future network generations. This paper offers a comprehensive state-of-the-art on HTS systems, focusing on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-generation satellite systems that we have named Extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed to maximize spectrum reuse and data rates and to flexibly steer the capacity to satisfy user demand. We introduce a novel architecture for future programmable regenerative payloads as well.
2024-12-31
IEEE Commun. Surv. Tutorials (publié)
doi.org
Exploiting Instruction-Following Retrievers for Malicious Information Retrieval
Parishad BehnamGhader
Nicholas Meade
Siva Reddy
Instruction-following retrievers have been widely adopted alongside LLMs in real-world applications, but little work has investigated the sa… (voir plus)fety risks surrounding their increasing search capabilities. We empirically study the ability of retrievers to satisfy malicious queries, both when used directly and when used in a retrieval augmented generation-based setup. Concretely, we investigate six leading retrievers, including NV-Embed and LLM2Vec, and find that given malicious requests, most retrievers can (for >50% of queries) select relevant harmful passages. For example, LLM2Vec correctly selects passages for 61.35% of our malicious queries. We further uncover an emerging risk with instruction-following retrievers, where highly relevant harmful information can be surfaced by exploiting their instruction-following capabilities. Finally, we show that even safety-aligned LLMs, such as Llama3, can satisfy malicious requests when provided with harmful retrieved passages in-context. In summary, our findings underscore the malicious misuse risks associated with increasing retriever capability.
2024-12-31
ACL (Findings) (publié)
doi.org
arxiv.org
FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting
He Zhu
Jun Bai
Na Li
Xiaoxiao Li
Dianbo Liu
David L. Buckeridge
Yue Li
Federated learning (FL) enables collaborative analysis of decentralized medical data while preserving patient privacy. However, the covariat… (voir plus)e shift from demographic and clinical differences can reduce model generalizability. We propose FedWeight, a novel FL framework that mitigates covariate shift by reweighting patient data from the source sites using density estimators, allowing the trained model to better align with the distribution of the target site. To support unsupervised applications, we introduce FedWeight ETM, a federated embedded topic model. We evaluated FedWeight in cross-site FL on the eICU dataset and cross-dataset FL between eICU and MIMIC III. FedWeight consistently outperforms standard FL baselines in predicting ICU mortality, ventilator use, sepsis diagnosis, and length of stay. SHAP-based interpretation and ETM-based topic modeling reveal improved identification of clinically relevant characteristics and disease topics associated with ICU readmission.
2024-12-31
npj Digital Medicine (publié)
doi.org
A "fine-cuts" approach disentangling psychopathic, autistic and alexithymic traits in their associations with affective, cognitive and motor empathy
Julia Ayache
Nikki Stevenson
Elisha Patel
Alexander Sumich
Guillaume Dumas
Nadja Heym
2024-12-31
Personality and Individual Differences (publié)
doi.org