Publications

Conditional Adversarial Random Forest for Synthetic Electronic Health Record Generation
Cynthia García Ybarra
Christian Gagné
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
openreview.net
openreview.net
CrediBench: Building Web-Scale Network Datasets for Information Integrity
Emma Kondrup
Sebastian Sabry
Hussein Abdallah
Zachary Yang
James Zhou
Kellin Pelrine
Jean-François Godbout
Michael M. Bronstein
Reihaneh Rabbany
Shenyang Huang
Online misinformation poses an escalating threat, amplified by the Internet's open nature and increasingly capable LLMs that generate persua… (see more)sive yet deceptive content. Existing misinformation detection methods typically focus on either textual content or network structure in isolation, failing to leverage the rich, dynamic interplay between website content and hyperlink relationships that characterizes real-world misinformation ecosystems. We introduce CrediBench: a large-scale data processing pipeline for constructing temporal web graphs that jointly model textual content and hyperlink structure for misinformation detection. Unlike prior work, our approach captures the dynamic evolution of general misinformation domains, including changes in both content and inter-site references over time. Our processed one-month snapshot extracted from the Common Crawl archive in December 2024 contains 45 million nodes and 1 billion edges, representing the largest web graph dataset made publicly available for misinformation research to date. From our experiments on this graph snapshot, we demonstrate the strength of both structural and webpage content signals for learning credibility scores, which measure source reliability. The pipeline and experimentation code are all available here, and the dataset is in this folder.
2025-09-21
NPGML @ Neural Information Processing Systems (poster)
doi.org
openreview.net
Extracting a COVID-19 signature from a multi-omic dataset
Baptiste Bauvin
Thibaud Godon
Guillaume Bachelot
Claudia Carpentier
Riikka Huusaari
Maxime Déraspe
Juho Rousu
Caroline Quach
Jacques Corbeil
The complexity of COVID-19 requires approaches that extend beyond symptom-based descriptors. Multi-omic data, combining clinical, proteomic,… (see more) and metabolomic information, offer a more detailed view of disease mechanisms and biomarker discovery. As part of a large-scale Quebec initiative, we collected extensive datasets from COVID-19 positive and negative patient samples. Using a multi-view machine learning framework with ensemble methods, we integrated thousands of features across clinical, proteomic, and metabolomic domains to classify COVID-19 status. We further applied a novel feature relevance methodology to identify condensed signatures. Our models achieved a balanced accuracy of 89% ± 5% despite the high-dimensional nature of the data. Feature selection yielded 12- and 50-feature signatures that improved classification accuracy by at least 3% compared to the full feature set. These signatures were both accurate and interpretable. This work demonstrates that multi-omic integration, combined with advanced machine learning, enables the extraction of robust COVID-19 signatures from complex datasets. The condensed biomarker sets provide a practical path toward improved diagnosis and precision medicine, representing a significant advancement in COVID-19 biomarker discovery.
2025-09-21
Frontiers in Bioinformatics (published)
doi.org
Graph Dreamer: Temporal Graph World Models for Sample-Efficient and Generalisable Reinforcement Learning
Anaïs Berkes
Donna Vakalis
Yoshua Bengio
David Rolnick
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
openreview.net
openreview.net
Intrinsic Meets Extrinsic Fairness: Assessing the Downstream Impact of Bias Mitigation in Large Language Models
Mina Arzaghi
Alireza Dehghanpour Farashah
Florian Carichon
Golnoosh Farnadi
Large Language Models (LLMs) are increasingly deployed in sensitive domains such as finance, where intrinsic representational biases can pro… (see more)pagate into extrinsic harms in downstream tasks. High-stakes applications such as credit scoring are especially vulnerable, as biased model behavior can reinforce existing inequities and result in harmful disparities across demographic groups \cite{blodgett2020language}. While prior research has questioned whether intrinsic bias truly translates into extrinsic unfairness \cite{goldfarb2020intrinsic}, this connection remains poorly understood. To address this gap, we propose a four-stage evaluation framework that systematically examines the relationship between intrinsic and extrinsic fairness. In Stage 1, we establish a baseline by training models such as logistic regression, LLM embeddings, and fine-tuned classifiers without any mitigation strategy, providing reference points for fairness and accuracy. In Stage 2, we evaluate task-level mitigation through Counterfactual Data Augmentation (CDA) \cite{gallegos2024bias}, which balances gender representation by generating counterfactual training instances, allowing us to assess improvements in extrinsic fairness. In Stage 3, we adapt concept unlearning \cite{dige2024mitigating} as an intrinsic bias mitigation method, encouraging LLMs to forget socioeconomic stereotypes while preserving fluency and predictive utility, and we evaluate how this intervention impacts downstream fairness. Finally, in Stage 4, we combine CDA with unlearning to test whether dual mitigation further enhances fairness. We conduct experiments on three datasets (Adult Census Income, ACS Employment, and German Credit) using instruction-tuned LLMs (LLaMA-3.1, Phi-3, and Gemma-2) in both frozen embedding and fine-tuned classifier settings, evaluating performance with predictive accuracy and group fairness metrics, including Demographic Parity, Accuracy Parity, and Equality of Odds. Our experiments demonstrate that intrinsic bias mitigation through unlearning is highly effective; in Phi-3, for instance, it reduces gender socioeconomic stereotype gaps by 94.9\% while maintaining language fluency. In downstream tasks, unlearning consistently improves group fairness metrics while preserving predictive accuracy, whereas CDA primarily enhances demographic parity but can introduce accuracy trade-offs. For instance, on the ACS Employment dataset, unlearned Gemma-2 improved Accuracy Parity from 0.199 to 0.104 (48\% gain), and combining CDA with unlearning on Llama-3.1 reduced Demographic Parity from 0.080 to 0.014 (82\% gain). On the Adult dataset, all three models maintained accuracy above 0.82 while showing reduced fairness gaps, and on German Credit, unlearning consistently outperformed CDA by improving group fairness metrics without sacrificing predictive performance. Overall, CDA and unlearning exhibit complementary effects, with their combination yielding the strongest fairness improvements across models and datasets. This work contributes to bias mitigation and fairness in LLMs in two ways. First, we adapt concept unlearning to mitigate socioeconomic stereotyping, showing that intrinsic bias reduction improves both representational and downstream fairness. Second, we introduce a unified evaluation framework that links intrinsic and extrinsic fairness, enabling systematic comparison of mitigation strategies. The framework is flexible, applying to both fine-tuned and frozen LLMs, and offers actionable guidance for deploying fairer models in finance and other high-stakes domains.
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
openreview.net
openreview.net
Modeling Open World Cognition as On-Demand Synthesis of Probabilistic Models
Lionel Wong
Katherine M. Collins
Lance Ying
Cedegao E. Zhang
Adrian Weller
Tobias Gerstenberg
Timothy J. O'Donnell
Alexander K. Lew
Jacob Andreas
Joshua B. Tenenbaum
Tyler BrookeWilson
When faced with novel situations, people can marshal relevant considerations from a wide range of background knowledge and use these for inf… (see more)erence and prediction. How do we draw in globally relevant information and reason over it coherently? We explore the hypothesis that people reason by constructing structured but small, ad-hoc mental models on the fly, tailored to novel situations. We propose a computational implementation of this idea -- a ``Model Synthesis Architecture'' (MSA) -- using language models to parameterize global, relevance-based retrieval of variables, and probabilistic programs to implement bespoke, coherent world models. We evaluate our MSA, along with ablations and baselines, as a model of human judgments across a sequence of experiments that requires progressively more open-ended and open-world reasoning about situations described in natural language. Across all experiments, the MSA captures human judgments, and outperforms the base LM alone – suggesting that MSAs offer a path towards capturing coherent human reasoning in open-ended domains.
2025-09-21
NeurIPS.cc/2025/Workshop/LAW (published)
openreview.net
openreview.net
Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets
Marylou Fauchard
Florian Carichon
Margarida Carvalho
Golnoosh Farnadi
2025-09-21
WiML @ Neural Information Processing Systems (published)
doi.org
openreview.net
Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models
Istabrak Abbes
Gopeshh Subbaraj
Matthew D Riemer
Nizar Islah
Benjamin Therien
Tsuguchika Tabaru
Hiroaki Kingetsu
A. Chandar
Irina Rish
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
doi.org
openreview.net
Reward the Reward Designer: Making Reinforcement Learning Useful for Clinical Decision Making
Sumana Basu
Adriana Romero
Doina Precup
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
openreview.net
openreview.net
Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework
Cléa Chataigner
Rebecca Ma
Prakhar Ganesh
Afaf Taïk
Elliot Creager
Golnoosh Farnadi
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
openreview.net
openreview.net
Towards Democratizing LLMs: Investigating Multilingual Mixture-of-Experts Models
Aditi Khandelwal
Marius Mosbach
Verna Dankers
Siva Reddy
Golnoosh Farnadi
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
openreview.net
openreview.net
Towards a generalizable, unified framework for decoding from multimodal neural activity
Nanda H Krishna
Mathys Loiselle
Avery Hee-Woon Ryoo
Matthew G Perich
Guillaume Lajoie
Recent advances in neural decoding have led to the development of large-scale deep learning-based neural decoders that can generalize across… (see more) sessions and subjects. However, existing approaches predominantly focus on single modalities of neural activity, limiting their applicability to specific modalities and tasks. In this work, we present a multimodal extension of the POYO framework that jointly processes neuronal spikes and local field potentials (LFPs) for behavioural decoding. Our approach employs flexible tokenization schemes for both spikes and LFPs, enabling efficient processing of heterogeneous neural populations without preprocessing requirements like binning. Through experiments on data from nonhuman primates performing motor tasks, we demonstrate that multimodal pretraining yields superior decoding performance compared to unimodal baselines. We also show evidence of cross-modal transfer: models pretrained on both modalities outperform LFP-only models when fine-tuned solely on LFPs, suggesting a path toward more cost-effective brain-computer interfaces that can use performant LFP-based decoders. Our models also exhibit robustness to missing modalities during inference when trained with modality masking, and scale effectively with both model size and pretraining data. Overall, this work represents an important first step towards unified, general-purpose neural decoders capable of leveraging diverse neural signals for a variety of brain-computer interface applications.
2025-09-21
NeurIPS.cc/2025/Workshop/BrainBodyFM (published)
openreview.net
openreview.net