Publications

A Guide to Misinformation Detection Data and Evaluation
Camille Thibault
Jacob-Junqi Tian
Gabrielle Peloquin-Skulski
Taylor Lynn Curtis
James Zhou
Florence Laflamme
Luke Yuxiang Guan
Reihaneh Rabbany
Jean-François Godbout
Kellin Pelrine
Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this, we h… (voir plus)ave curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of 36 datasets that consist of statements or claims, as well as the 9 datasets that consist of data in purely paragraph form. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as spurious correlations, or examples that are ambiguous or otherwise impossible to assess for veracity. We find the latter issue is particularly severe and affects most datasets in the literature. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. Finally, we propose and highlight Evaluation Quality Assurance (EQA) as a tool to guide the field toward systemic solutions rather than inadvertently propagating issues in evaluation. Overall, this guide aims to provide a roadmap for higher quality data and better grounded evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.
2025-08-02
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (publié)
doi.org
openreview.net
Responsible AI Day
Ebrahim Bagheri
Faezeh Ensan
Calvin Hillis
Reihaneh Rabbany
Robin Cohen
Benjamin C. M. Fung
Sébastien Gambs
This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore h… (voir plus)ow data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.
2025-08-02
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Michael M. Bronstein
2025-08-02
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (publié)
doi.org
Unified Game Moderation: Soft-Prompting and LLM-Assisted Label Transfer for Resource-Efficient Toxicity Detection
Zachary Yang
Domenico Tullo
Reihaneh Rabbany
Toxicity detection in gaming communities faces significant scaling challenges when expanding across multiple games and languages, particular… (voir plus)ly in real-time environments where computational efficiency is crucial. We present two key findings to address these challenges while building upon our previous work on ToxBuster, a BERT-based real-time toxicity detection system. First, we introduce a soft-prompting approach that enables a single model to effectively handle multiple games by incorporating game-context tokens, matching the performance of more complex methods like curriculum learning while offering superior scalability. Second, we develop an LLM-assisted label transfer framework using GPT-4o-mini to extend support to seven additional languages. Evaluations on real game chat data across French, German, Portuguese, and Russian achieve macro F1-scores ranging from 32.96% to 58.88%, with particularly strong performance in German, surpassing the English benchmark of 45.39%. In production, this unified approach significantly reduces computational resources and maintenance overhead compared to maintaining separate models for each game and language combination. At Ubisoft, this model successfully identifies an average of 50 players, per game, per day engaging in sanctionable behavior.
2025-08-02
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (publié)
doi.org
arxiv.org
A Unified Solution to Diverse Heterogeneities in One-Shot Federated Learning
Jun Bai
Yiliao Song
Di Wu
Atul Sajjanhar
Yong Xiang
Wei Zhou
Xiaohui Tao
Yan Li
Yue Li
One-Shot Federated Learning (OSFL) restricts communication between the server and clients to a single round, significantly reducing communic… (voir plus)ation costs and minimizing privacy leakage risks compared to traditional Federated Learning (FL), which requires multiple rounds of communication. However, existing OSFL frameworks remain vulnerable to distributional heterogeneity, as they primarily focus on model heterogeneity while neglecting data heterogeneity. To bridge this gap, we propose FedHydra, a unified, data-free, OSFL framework designed to effectively address both model and data heterogeneity. Unlike existing OSFL approaches, FedHydra introduces a novel two-stage learning mechanism. Specifically, it incorporates model stratification and heterogeneity-aware stratified aggregation to mitigate the challenges posed by both model and data heterogeneity. By this design, the data and model heterogeneity issues are simultaneously monitored from different aspects during learning. Consequently, FedHydra can effectively mitigate both issues by minimizing their inherent conflicts. We compared FedHydra with five SOTA baselines on four benchmark datasets. Experimental results show that our method outperforms the previous OSFL methods in both homogeneous and heterogeneous settings. The code is available at https://github.com/Jun-B0518/FedHydra.
2025-08-02
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
arxiv.org
Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis in preclinical models
Darcy Elizabeth Wagner
Hani N. Alsafadi
Nilay Mitash
Aurelien Justet
Qianjiang Hu
Ricardo Pineda
Claudia Staab-Weijnitz
Martina Korfei
Nika Gvazava
Kristin Wannemo
Ugochi Onwuka
Molly Mozurak
Adriana Estrada-Bernal
Juan Cala Garcia
Katrin Mutze
Rita Costa
Deniz Bölükbas
John Stegmayr
Wioletta Skronska-Wasek
Stephan Klee … (voir 14 de plus)
Chiharu Ota
Hoeke A. Baarsma
Jingtao Wang
John Sembrat
Anne Hilgendorff
Jun Ding
Andreas Günther
Rachel Chambers
Ivan O Rosas
Stijn de Langhe
Naftali Kaminski
Mareike Lehmann
Oliver Eickelberg
Melanie Königshoff
Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix deposition. Current … (voir plus)IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive extracellular matrix production in IPF. Here we show that fibrotic alveolar type II cells regulate production and crosslinking of extracellular matrix via the co-transcriptional activator YAP. YAP leads to increased expression of Lysl oxidase (LOX) and subsequent LOX-mediated crosslinking by fibrotic alveolar type II cells. Pharmacological YAP inhibition via verteporfin reverses fibrotic alveolar type II cell reprogramming and LOX expression in experimental lung fibrosis in vivo and in human fibrotic tissue ex vivo. We thus identify YAP-TEAD/LOX inhibition in alveolar type II cells as a promising potential therapy for IPF patients.
2025-08-01
Nature Communications (publié)
doi.org
ConvNTC: convolutional neural tensor completion for detecting “A–A–B” type biological triplets
Pei Liu
Xiao Liang
Yuemei Li
Jiawei Luo
Abstract Systematically investigating interactions among molecules of the same type across different contexts is crucial for unraveling dise… (voir plus)ase mechanisms and developing potential therapeutic strategies. The “A–A–B” triplet paradigm provides a principled approach to model such context-specific interactions, and leveraging third-order tensor to capture such type ternary relationships is an efficient strategy. However, effectively modeling both multilinear and nonlinear characteristics to accurately identify such triplets using tensor-based methods remains a challenge. In this paper, we propose a novel Convolutional Neural Tensor Completion (ConvNTC) framework that collaboratively learns the multilinear and nonlinear representations to model triplet-based network interactions. ConvNTC consists of a multilinear module and a nonlinear module. The former is a tensor decomposition approach that integrates multiple constraints to learn the tensor factor embeddings. The latter contains three components: an embedding generator to produce position-specific index embeddings for each tensor entry in addition to the factor embeddings, a convolutional encoder to perform nonlinear feature mapping while preserving the tensor’s rank-one property, and a Kolmogorov–Arnold Network (KAN) based predictor to effectively capture high-dimensional relationships aligned with the intrinsic structure of real-world data. We evaluate ConvNTC on two types triplet datasets of the “A–A–B” type: miRNA–miRNA–disease and drug–drug–cell. Comprehensive experiments against 11 state-of-the-art methods demonstrate the superiority of ConvNTC in terms of triplet prediction. ConvNTC reveals promising prognostic values of the miRNA–miRNA interactions on breast cancer and detects synergistic drug combinations in cancer cell lines.
2025-07-31
Briefings in Bioinformatics (publié)
doi.org
Data Visualization using Functional Data Analysis
Haozhe Chen
Andres Duque Correa
Guy Wolf
Kevin R. Moon
Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many ex… (voir plus)isting methods fail to capture the underlying structure of the data. Furthermore, existing methods that can theoretically eliminate all noise are difficult to implement in high dimensions. Here we propose a new data visualization method called Functional Information Geometry (FIG) for dynamical processes that denoises the data by leveraging time information and mitigates the curse of dimensionality using approaches from functional data analysis. We experimentally demonstrate that FIG outperforms other methods in terms of capturing the true structure, hyperparameter robustness, and computational speed. We then use our method to visualize EEG brain measurements of sleep activity.
2025-07-31
2025 International Conference on Sampling Theory and Applications (SampTA) (publié)
doi.org
openreview.net
Efficient Deep Reinforcement Learning-Based Supplementary Damping Control with a Coordinated RMS Training and EMT Testing Scheme
Tao Xue
Mingxuan Zhao
Ilhan Kocar
Mohsen Ghafouri
Antoine Lesage-Landry
Siqi Bu
Ziqing Zhu
2025-07-31
IEEE Transactions on Power Delivery (publié)
doi.org
Towards an Interpretable Machine Learning Model for Predicting Antimicrobial Resistance
Mohamed Mediouni
Vladimir Makarenkov
Abdoulaye Banire Diallo
2025-07-31
Journal of Global Antimicrobial Resistance (publié)
doi.org
Zero-Shot Anomaly Detection with Dual-Branch Prompt Learning
Zihan Wang
S Ebrahimi Kahou
Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable featur… (voir plus)es rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.
2025-07-31
ArXiv (prépublication)
doi.org
arxiv.org
Advancing science- and evidence-based AI policy.
Rishi Bommasani
Sanjeev Arora
Jennifer Chayes
Yejin Choi
Mariano-Florentino Cuéllar
Li Fei-Fei
Daniel E. Ho
Dan Jurafsky
Sanmi Koyejo
Hima Lakkaraju
Arvind Narayanan
Alondra Nelson
Emma Pierson
Joelle Pineau
Scott Singer
Gael Varoquaux
Suresh Venkatasubramanian
Ion Stoica
Percy Liang
Dawn Song
2025-07-30
Science (publié)
doi.org
arxiv.org