Publications

Empowering 2D neural network for 3D medical image segmentation via neighborhood information fusion
Qiankun Li
Xiaolong Huang
Yani Zhang
Bo Fang
Duo Hong
Junxin Chen
2026-01-01
Pattern Recognition (published)
doi.org
Active Learning with Non-Uniform Costs for African Natural Language Processing
Bonaventure F. P. Dossou
Ines Arous
Audrey Durand
Jackie Chi Kit Cheung
2025-12-31
Conference of the European Chapter of the Association for Computational Linguistics (published)
doi.org
Additional methodological and statistical details from Testing the scale dependence of plant community assembly processes using imaging spectroscopy
Anna L. Crofts
J. Pablo Arroyo-Mora
Margaret Kalacska
Étienne Laliberté
Mark Vellend
Additional methodological details (including, Figure S1-S2) and detailed statistical results (Figure S3-S4 and Table S1-S3).
2025-12-31
Figshare (unknown)
doi.org
AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages
Hao Yu
Tianyi Xu
Michael A. Hedderich
Wassim Hamidouche
Syed Waqas Zamir
David Ifeoluwa Adelani
2025-12-31
arXiv (published)
doi.org
arxiv.org
Algorithmic Fairness Across Alignment Procedures and Agentic Systems
Zeyu Tang
Prakhar Ganesh
Awa Dieng
Miriam Rateike
Jamelle Watson-Daniels
Golnoosh Farnadi
Jessica Schrouff
Sanmi Koyejo
AI has transitioned from predictive models to interactive, autonomous agents capable of reasoning, planning, and executing complex goals. As… (see more) the systems increasingly influence social, economic, and scientific decisions, they determine whose interests are represented and whose opportunities are constrained. Ensuring fairness, therefore, is no longer an ethical preference but a practical imperative. As the fairness challenges are fundamentally transformed by advanced AI systems, traditional algorithmic fairness frameworks developed primarily for prediction and/or prediction-based decision-making no longer suffice. This workshop, _Algorithmic Fairness Across Alignment Procedures and Agentic Systems_ (AFAA), emerges at this pivotal moment as a timely forum for rethinking fairness in AI alignment processes and agentic system development. By examining fairness across alignment procedures and agentic systems, this workshop creates a crucial platform for bridging the gap between rapid technical advances in model capabilities and the equally important advances needed in frameworks of algorithmic fairness to govern these powerful systems.
2025-12-31
Workshop Proposals @ International Conference on Learning Representations (published)
openreview.net
openreview.net
An Area-Efficient Routing Solution for Automorphism Ensemble Decoding of Polar Codes
Jiajie Li
Huayi Zhou
Ryan Seah
Marwan Jalaleddine
Warren J. Gross
2025-12-31
IEEE Transactions on Communications (published)
doi.org
ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning
Roger Creus Castanyer
Faisal Mohamed
Pablo Samuel Castro
Cyrus Neary
Glen Berseth
Reinforcement learning (RL) algorithms are highly sensitive to reward function specification, which remains a central challenge limiting the… (see more)ir broad applicability. We present ARM-FM: Automated Reward Machines via Foundation Models, a framework for automated, compositional reward design in RL that leverages the high-level reasoning capabilities of foundation models (FMs). Reward machines (RMs) - an automata-based formalism for reward specification - are used as the mechanism for RL objective specification, and are automatically constructed via the use of FMs. The structured formalism of RMs yields effective task decompositions, while the use of FMs enables objective specifications in natural language. Concretely, we (i) use FMs to automatically generate RMs from natural language specifications; (ii) associate language embeddings with each RM automata-state to enable generalization across tasks; and (iii) provide empirical evidence of ARM-FM's effectiveness in a diverse suite of challenging environments, including evidence of zero-shot generalization.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera
Jacopo Andreoli
Davide Dalle Pezze
Mirco Ravanaelli
Gian Antonio Susto
A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has … (see more)improved considerably upon traditional model-based and statistical approaches in terms of predictive performance. However, in order to optimally plan maintenance operations, it is also important to quantify the uncertainty inherent to the predictions. This issue can be addressed by turning standard frequentist neural networks into Bayesian neural networks, which are naturally capable of providing confidence intervals around the estimates. Several methods exist for training those models. Researchers have focused mostly on parametric variational inference and sampling-based techniques, which notoriously suffer from limited approximation power and large computational burden, respectively. In this work, we use Stein variational gradient descent, a recently proposed algorithm for approximating intractable distributions that overcomes the drawbacks of the aforementioned techniques. In particular, we show through experimental studies on simulated run-to-failure turbofan engine degradation data that Bayesian deep learning models trained via Stein variational gradient descent consistently outperform with respect to convergence speed and predictive performance both the same models trained via parametric variational inference and their frequentist counterparts trained via backpropagation. Furthermore, we propose a method to enhance performance based on the uncertainty information provided by the Bayesian models. We release the source code at https://github.com/lucadellalib/bdl-rul-svgd.
2025-12-31
IEEE Trans Autom. Sci. Eng. (published)
doi.org
arxiv.org
Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries
Divyat Mahajan
Sachin Goyal
Badr Youbi Idrissi
Mohammad Pezeshki
Ioannis Mitliagkas
David Lopez-Paz
Kartik Ahuja
Next-token prediction (NTP) has driven the success of large language models (LLMs), but it struggles with long-horizon reasoning, planning, … (see more)and creative writing, with these limitations largely attributed to teacher-forced training. Multi-token prediction (MTP) partially mitigates these issues by predicting several future tokens at once, but it mostly captures short-range dependencies and offers limited improvement. We propose future summary prediction (FSP), which trains an auxiliary head to predict a compact representation of the long-term future, preserving information relevant for long-form generations. We explore two variants of FSP: handcrafted summaries, for example, a bag of words summary of the future of the sequence, and learned summaries, which use embeddings produced by a reverse language model trained from right to left. Large-scale pretraining experiments (3B and 8B-parameter models) demonstrate that FSP provides improvements over both NTP and MTP across math, reasoning, and coding benchmarks.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
A Capacitated Collection-and-Delivery-Point Location Problem with Random Utility Maximizing Customers
David Pinzon Ulloa
Ammar Metnani
Emma Frejinger
2025-12-31
Comput. Oper. Res. (published)
doi.org
arxiv.org
Clinicians' ethical considerations on use of AI-enabled technologies for primary prevention of cardiovascular disease in female patients
Ting Wang
Amrita Sandhu
Kyle Vamvakas
Howard Bergman
Roland Grad
Isabelle Vedel
Marie-Pierre Gagnon
Shahram Yousefi
Samira Abbasgholizadeh-Rahimi
Artificial intelligence (AI) is increasingly used in healthcare to support the prevention and management of cardiovascular disease (CVD); ho… (see more)wever, its ethical implications in clinical practice, particularly for female patients, remain insufficiently explored. This study aimed to explore clinicians' perspectives on the ethical use of AI for preventing and managing cardiovascular disease (CVD) in female patients. A qualitative descriptive design was employed using semi-structured interviews with clinicians practicing in Montreal, Canada. Interviews were conducted online, audio-recorded with participants’ consent, and transcribed for analysis. Data were analyzed using deductive thematic analysis informed by ethical domains in the established AI frameworks. Ethical approval was obtained from McGill University’s Research Ethics Board. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. A final sample of twelve clinicians was interviewed, with each interview lasting approximately 60 minutes. Four key themes emerged: fairness, privacy and security, explainability, and data integrity. Clinicians expressed concerns that AI-enabled technologies may introduce or reinforce biases affecting certain populations, including older adults, individuals with limited digital literacy, and those lacking reliable internet access or access to digital technologies. Participants also raised concerns regarding data integrity, privacy and security, and emphasized the importance of transparent and understandable AI outputs to support clinical decision-making. Ethical considerations are fundamental to the responsible integration of AI in cardiovascular care. Addressing concerns related to fairness, privacy and security, explainability, and data integrity may strengthen clinician trust and support the implementation of AI-enabled technologies in clinical practice. Future research should explore practical approaches to address these concerns and assess how ethically informed AI systems can be implemented effectively in clinical practice.
2025-12-31
FACETS (published)
doi.org
Clinicians' needs and perspectives on use of AI-enabled technologies for primary prevention of cardiovascular disease in female patients
Ting Wang
Amrita Sandhu
Kyle Vamvakas
Howard Bergman
Roland Grad
Isabelle Vedel
Marie-Pierre Gagnon
Shahram Yousefi
Samira Abbasgholizadeh-Rahimi
This study aimed to (1) explore clinicians’ perspectives of cardiovascular disease (CVD) and risk management in female patients and (2) de… (see more)scribe clinicians’ needs and desired features in AI-enabled tools for primary prevention and management of CVD among female patients. This work employed a qualitative description design. We conducted semi-structured interviews with 12 clinicians in Montreal, Canada. We used inductive thematic analysis to interpret the data. Seven themes emerged from the analysis. Three themes were related to the first objective: complexity in clinical decision-making, limitations of CVD risk assessment tools, and resources and health literacy. Four themes were related to the second objective: AI efficiency, multilingual design, electronic medical record integration, and ease of use. Clinicians reported challenges in supporting female patients at higher risk for CVD and expressed concerns about existing decision support tools. They showed openness to AI-enabled tools like Xi-Care and provided input on desired features to ensure their usability and effectiveness. There is a demand to support clinicians in the primary prevention and management of CVD among female patients. AI-enabled tools could effectively address this demand, provided their development prioritizes clinicians' needs and perspectives to ensure safe and effective implementation.
2025-12-31
FACETS (published)
doi.org