Publications

What Matters when Modeling Human Behavior using Imitation Learning?
Aneri Muni
Esther Derman
Vincent Taboga
Pierre-Luc Bacon
Erick Delage
As AI systems become increasingly embedded in human decision-making process, aligning their behavior with human values is critical to ensuri… (voir plus)ng safe and trustworthy deployment. A central approach to AI Alignment called Imitation Learning (IL), trains a learner to directly mimic desirable human behaviors from expert demonstrations. However, standard IL methods assume that (1) experts act to optimize expected returns; (2) expert policies are Markovian. Both assumptions are inconsistent with empirical findings from behavioral economics, according to which humans are (1) risk-sensitive; and (2) make decisions based on past experience. In this work, we examine the implications of risk sensitivity for IL and show that standard approaches do not capture all optimal policies under risk-sensitive decision criteria. By characterizing these expert policies, we identify key limitations of existing IL algorithms in replicating expert performance in risk-sensitive settings. Our findings underscore the need for new IL frameworks that account for both risk-aware preferences and temporal dependencies to faithfully align AI behavior with human experts.
2025-06-09
ICML.cc/2025/Workshop/MoFA (poster)
openreview.net
openreview.net
Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Suyuchen Wang
Jinlin Wang
Xinyu Wang
Shiqi Li
Xiangru Tang
Sirui Hong
Xiao-Wen Chang
Chenglin Wu
Bang Liu
Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on prov… (voir plus)ided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities. Our method requires minimal labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain. Extensive experiments on multiple real-world and counterfactual QA benchmarks demonstrate that our approach substantially outperforms supervised fine-tuning, traditional retrieval-augmented generation methods, and external retrieval solutions. This work represents a fundamental advancement in making LLMs more accurate, reliable, and efficient for knowledge-intensive tasks.
2025-06-08
ICML.cc/2025/Workshop/LCFM (accepté)
openreview.net
openreview.net
A Meta-Learning Approach to Causal Inference
Dragos Cristian Manta
Philippe Brouillard
Dhanya Sridhar
Predicting the effect of unseen interventions is at the heart of many scientific endeavours. While causal discovery is often used to answer … (voir plus)these causal questions, it involves learning a full causal model, not tailored to the specific goal of predicting unseen interventions, and operates under stringent assumptions. We introduce a novel method based on meta-learning that predicts interventional effects without explicitly assuming a causal model. Our preliminary results on synthetic data show that it can provide good generalization to unseen interventions, and it even compares favorably to a causal discovery method. Our model-agnostic method opens up many avenues for future exploration, particularly for settings where causal discovery cannot be applied.
2025-06-08
ICML.cc/2025/Workshop/SIM (poster)
openreview.net
openreview.net
PyLO: Towards Accessible Learned Optimizers in PyTorch
Paul Janson
Benjamin Therien
Quentin Gregory Anthony
Xiaolong Huang
Abhinav Moudgil
Eugene Belilovsky
Learned optimizers have been an active research topic over the past decade, with increasing progress toward practical, general-purpose optim… (voir plus)izers that can serve as drop-in replacements for widely used methods like Adam. However, recent advances -- such as VeLO, which was meta-trained for 4000 TPU-months -- remain largely inaccessible to the broader community, in part due to their reliance on JAX and the absence of user-friendly packages for applying the optimizers after meta-training. To address this gap, we introduce PyLO, a PyTorch-based library that brings learned optimizers to the broader machine learning community through familiar, widely adopted workflows. Unlike prior work focused on synthetic or convex tasks, our emphasis is on applying learned optimization to real-world large-scale pre-training tasks. Our release includes a CUDA-accelerated version of the small_fc_lopt learned optimizer architecture from (Metz et al., 2022a), delivering substantial speedups -- from 39.36 to 205.59 samples/sec throughput for training ViT B/16 with batch size 32. PyLO also allows us to easily combine learned optimizers with existing optimization tools such as learning rate schedules and weight decay. When doing so, we find that learned optimizers can substantially benefit. Our code is available at https://github.com/Belilovsky-Lab/pylo
2025-06-08
ICML.cc/2025/Workshop/CODEML (publié)
doi.org
openreview.net
Quantized Disentanglement: A Practical Approach
Vitória Barin-Pacela
Kartik Ahuja
Simon Lacoste-Julien
P Vincent
2025-06-08
ICML.cc/2025/Workshop/SIM (poster)
openreview.net
openreview.net
Revisiting the Goldilocks Zone in Inhomogeneous Networks
Zacharie Garnier Cuchet
A. Chandar
Ekaterina Lobacheva
We investigate how architectural inhomogeneities—such as biases, layer normalization, and residual connections—affect the curvature of t… (voir plus)he loss landscape at initialization and its link to trainability. We focus on the Goldilocks zone, a region in parameter space with excess positive curvature, previously associated with improved optimization in homogeneous networks. To extend this analysis, we compare two scaling strategies: weight scaling and softmax temperature scaling. Our results show that in networks with biases or residual connections, both strategies identify a Goldilocks zone aligned with better training. In contrast, layer normalization leads to lower or negative curvature, yet stable optimization—revealing a disconnect between curvature and trainability. Softmax temperature scaling behaves more consistently across models, making it a more robust probe. Overall, the Goldilocks zone remains relevant in inhomogeneous networks, but its geometry and predictive power depend on architectural choices, particularly normalization.
2025-06-08
ICML.cc/2025/Workshop/HiLD (poster)
openreview.net
openreview.net
Spaced Scheduling for Large Language Model Training
Amine El hattami
Nicolas Chapados
Christopher Pal
2025-06-08
TMLR (accepté)
openreview.net
openreview.net
TGM: A Modular Framework for Machine Learning on Temporal Graphs
Jacob Chmura
Shenyang Huang
Ali Parviz
Farimah Poursafaei
Michael M. Bronstein
Guillaume Rabusseau
Matthias Fey
Reihaneh Rabbany
While deep learning on static graphs has been revolutionized by standardized libraries like PyTorch Geometric and DGL, machine learning on T… (voir plus)emporal Graphs (TG), networks that evolve over time, lacks comparable software infrastructure. Existing TG libraries are limited in scope, focusing on a single method category or specific algorithms. We introduce Temporal Graph Modelling (TGM), a comprehensive framework for machine learning on temporal graphs to address this gap. Through a modular architecture, TGM is the first library to support both discrete and continuous-time TG methods and implements a wide range of TG methods. The TGM framework combines an intuitive front-end API with an optimized backend storage, enabling reproducible research and efficient experimentation at scale. Key features include graph-level optimizations for offline training and built-in performance profiling capabilities. Through extensive benchmarking on five real-world networks, TGM is up to 6 times faster than the widely used DyGLib library on TGN and TGAT models and up to 8 times faster than the UTG framework for converting edges into coarse-grained snapshots.
2025-06-08
ICML.cc/2025/Workshop/CODEML (publié)
openreview.net
openreview.net
Towards Fair In-Context Learning with Tabular Foundation Models
Patrik Joslin Kenfack
S Ebrahimi Kahou
Ulrich Matchi Aïvodji
Tabular foundational models have shown promising in-context learning capabilities on structured data by using training examples as context w… (voir plus)ithout further parameter adjustments. This emerging approach positions itself as a competitive alternative to traditional gradient-boosted tree methods. However, while biases in conventional machine learning models are well documented, it remains unclear how these biases manifest in Tabular ICL. The paper investigates the fairness implications of Tabular ICL and explores three preprocessing strategies—correlation removal, group-balanced demonstration selection, and uncertainty-based demonstration selection—to address bias. Comprehensive experiments indicate that uncertainty-based demonstration selection consistently enhances group fairness in the predictions. The source code for reproducing the results of this work can be found at https://anonymous.4open.science/r/Fair-TabICL-DD84.
2025-06-08
ICML.cc/2025/Workshop/FMSD (publié)
doi.org
openreview.net
Two-point deterministic equivalence for SGD in random feature models
Alexander Atanasov
Blake Bordelon
Jacob A Zavatone-Veth
Courtney Paquette
Cengiz Pehlevan
2025-06-08
ICML.cc/2025/Workshop/HiLD (poster)
openreview.net
openreview.net
Ultrasound and MRI-based evaluation of relationships between morphological and mechanical properties of the lower lumbar multifidus muscle in chronic low back pain
Neda Naghdi
Sara Masi
Cleo Bertrand
Brent Rosenstein
Julien Cohen-Adad
Hassan Rivaz
Mathieu Roy
Maryse Fortin
While lumbar multifidus (MF) muscle alterations are linked to low back pain (LBP), the structure-function relationship is not fully understo… (voir plus)od. This study aims to evaluate the relationship between fatty degeneration of the lumbar MF muscle and its function in individuals with and without LBP. The study included 25 participants with chronic nonspecific LBP and 25 age- and sex-matched healthy controls. Participants underwent MRI assessment for MF fat infiltration, utilizing IDEAL fat-water images. Ultrasound measures evaluated MF function, including shear-wave elastography (SWE) for stiffness/elasticity and thickness ratio from rest to submaximal contraction. All measurements were acquired at L4/L5 and L5/S1 spinal levels, bilaterally. Bivariate and multivariable linear regression models were used to assess the relationship between morphology and function, while age, sex, body max index (BMI), physical activity levels, and LBP status were considered as covariates. Fifty participants (26 females) were included (mean age: 39.22 ± 11.67). Greater % MF fat at L4/L5 was significantly associated with greater MF SWE ratio (p = 0.002). No significant bivariate or multivariable relationships were found between MF fat infiltration and MF thickness ratio. Participants with LBP exhibited lower contraction ratios (p = 0.017) and higher SWE during contraction (p = 0.03) at L4/L5 compared to controls. This study highlights a positive association between MF fat infiltration and SWE-based stiffness measures at L4/L5, suggesting altered muscle composition may impacts MF function. However, no relationship was found between MF fat infiltration and contraction. Participants with LBP demonstrated distinct deficits in muscle activation, supporting the need for targeted rehabilitation strategies addressing these functional impairments.
2025-06-08
European Spine Journal (publié)
doi.org
Multi-Priority Scheduling for Traffic Management in Future Scalable Payloads
Zineb Garroussi
Olfa Ben Yahia
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
Guillaume Mantelet
Antoine Lesage-Landry
Gunes Karabulut Kurt
Through multibeam, frequency reuse, and advanced antenna technology, regenerative non-geostationary orbit (NGSO) extremely high-throughput s… (voir plus)atellites (EHTS) are expected to play a key role in future communications, delivering data rates up to terabits per second. This paper investigates a novel architecture for future regenerative and scalable payloads to satisfy users’ demands for varying quality of service (QoS). This architecture is designed based on multiple modem banks and requires a new flow assignment strategy to efficiently route traffic within the satellite. We propose a multi-commodity path flow optimization problem to manage the load with varying QoS requirements across multiple modems within an NGSO high-throughput satellite (HTS) system and beyond. The simulation results demonstrate that the proposed model consistently maintains low delays and packet losses for the highest-priority traffic and outperforms the classical first-in, first-out (FIFO) approach.
2025-06-07
2025 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)
doi.org