Christian Gagné

Biographie

Christian Gagné est professeur au Département de génie électrique et de génie informatique de l’Université Laval depuis 2008, et dirige l’Institut intelligence et données (IID). Il détient une chaire en IA Canada-CIFAR et est membre associé à Mila – Institut québécois d’intelligence artificielle. Il est également membre du Laboratoire de vision et systèmes numériques (LVSN), une composante du Centre de recherche en robotique, vision et intelligence machine (CeRVIM) ainsi que du Centre de recherche en données massives (CRDM) de l’Université Laval. Il fait partie des regroupements stratégiques REPARTI et UNIQUE du Fonds de recherche du Québec – Nature et technologies (FRQNT), du centre VITAM du Fonds de recherche du Québec – Santé (FRQS) et de l’Observatoire international sur les impacts sociétaux de l’IA et du numérique (OBVIA).

Ses intérêts de recherche portent sur l’élaboration de méthodes pour l’apprentissage automatique et l’optimisation stochastique. En particulier, il se consacre aux réseaux de neurones profonds, à l’apprentissage et au transfert de représentations, au méta-apprentissage ainsi qu’à l’apprentissage multitâche. Il s’intéresse également aux approches d’optimisation basées sur des modèles probabilistes ainsi qu’aux algorithmes évolutionnaires, entre autres pour l’optimisation boîte noire et la programmation automatique. Une part importante de ses travaux porte également sur la mise en pratique de ces techniques dans des domaines comme la vision numérique, la microscopie, la santé, l’énergie et les transports.

Étudiants actuels

Frédéric Beaupré

Doctorat - Université Laval

Catherine Bouchard

Doctorat - Université Laval

Olivier Bussière

Maîtrise recherche - Université Laval

Sara Karami

Doctorat - Université Laval

Rose Nayoung Kwon

Doctorat - Université Laval

Benjamin Léger

Doctorat - Université Laval

Samy Mammeri

Baccalauréat - Université Laval

Jonas Ngnawe

Doctorat - Université Laval

Doctorat - Université Laval

Site web

Adam Tupper

Doctorat - Université Laval

Site web

Publications

Hidden-State Similarity Predicts Re-Elicitation After Inoculation Prompting

Fine-tuning on narrow harmful tasks can cause emergent misalignment, where models generalize harmful behavior beyond the training distributi… (voir plus)on. Inoculation prompting can reduce this effect by explicitly eliciting the undesired behavior during training, but recent work shows that the behavior can reappear when evaluation prompts contain cues from the training context. We study what makes such prompts effective triggers. We find that textual similarity to the inoculation prompt is an incomplete predictor: prompts are more likely to re-elicit suppressed behavior when they induce activation states similar to those produced by the inoculation context. These findings advance our understanding of how inoculation prompting modulates conditional misalignment, and suggest that activation-space analysis can help identify when suppressed behaviors remain accessible under eval-time prompts.

2026-06-10

ICML.cc/2026/Workshop/Mech_Interp (poster)

When Does Interleaving Prevent Emergent Misalignment?

Chen Sun

Large language models finetuned on narrow harmful tasks are prone to emergent misalignment (EM), where harmful behavior generalizes beyond t… (voir plus)he training distribution. Interleaving benign data during finetuning has been proposed as a mitigation, but recent work disagrees on whether it prevents EM. In this paper, we investigate this disagreement on Qwen-2.5 7B and 32B, and find that no single property of the interleaved data, taken in isolation, accounts for the gap. Instead, much of it traces to the evaluation itself, as the standard EM benchmark is sensitive to the length of the prompts it uses, and lengthening the evaluation prompts substantially shifts measured misalignment across model sizes. We then identify a region in the model's activations that predicts whether a given interleaved set will prevent EM, and show that reformulating benign data to fall within it substantially reduces EM on both 7B and 32B. This suggests that the standard EM benchmark, which relies on short prompts, may misrepresent the effectiveness of proposed mitigations.

2026-06-10

ICML.cc/2026/Workshop/Mech_Interp (poster)

Reliability-Gated Source Anchoring for Continual Test-Time Adaptation

Vikash Singh

Debargha Ganguly

Weicong Chen

Sabyasachi Sahoo

Sreehari Sankar

Biyao Zhang

Mohsen Hariri

Shouren Wang

Osama Zafar

Vipin Chaudhary

Continual test-time adaptation (CTTA) updates a pretrained model online on an unlabeled, non-stationary stream while anchoring it to a froze… (voir plus)n source checkpoint. This anchor is useful only when the source remains reliable. On CCC-Hard, however, a ResNet-50 source falls to approximately

2026-05-12

arXiv (prépublication)

arxiv.org

Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization

Benjamin Léger

Kazem Meidani

Symbolic regression (SR) aims to discover mathematical expressions from data, a task traditionally tackled using Genetic Programming (GP) th… (voir plus)rough combinatorial search over symbolic structures. Latent Space Optimization (LSO) methods use neural encoders to map symbolic expressions into continuous spaces, transforming the combinatorial search into continuous optimization. SNIP (Meidani et al., 2024), a contrastive pre-training model inspired by CLIP, advances LSO by introducing a multi-modal approach: aligning symbolic and numeric encoders in a shared latent space to learn the phenotype-genotype mapping, enabling optimization in the numeric space to implicitly guide symbolic search. However, this relies on fine-grained cross-modal alignment, whereas literature on similar models like CLIP reveals that such an alignment is typically coarse-grained. In this paper, we investigate whether SNIP delivers on its promise of effective bi-modal optimization for SR. Our experiments show that: (1) cross-modal alignment does not improve during optimization, even as fitness increases, and (2) the alignment learned by SNIP is too coarse to efficiently conduct principled search in the symbolic space. These findings reveal that while multi-modal LSO holds significant potential for SR, effective alignment-guided optimization remains unrealized in practice, highlighting fine-grained alignment as a critical direction for future work.

2026-04-08

arXiv (prépublication)

arxiv.org

Ca2+ transient detection and segmentation with the Astronomically motivated algorithm for Background Estimation And Transient Segmentation (Astro-BEATS)

Bi Fan

Anthony Bilodeau

Frédéric Beaupré

Theresa Wiesner

Flavie Lavoie-Cardinal

Renée Hložek

Fluorescence-based Ca…

2026-03-18

arXiv (prépublication)

Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling

Ola Ahmad

Frédéric Precioso

Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims… (voir plus) to simultaneously achieve adaptation to a downstream task and robustness to adversarial examples, remains challenging. Despite the abundance of non-robust pretrained models in open-source repositories, their potential for RFT is less understood. We address this knowledge gap by systematically examining RFT from such non-robust models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub _suboptimal transfer_. In challenging scenarios (eg, difficult tasks, high perturbation), the resulting performance can be so low that it may be considered a transfer failure. We find that fine-tuning using a robust objective impedes task adaptation at the beginning of training and eventually prevents optimal transfer. However, we propose a novel heuristic, _Epsilon-Scheduling_, a schedule over perturbation strength used during training that promotes optimal transfer. Additionally, we introduce _expected robustness_, a metric that captures performance across a range of perturbations, providing a more comprehensive evaluation of the accuracy-robustness trade-off of diverse models at test-time. Extensive experiments on wide range of configurations (six pretrained models and five datasets) show that _Epsilon-Scheduling_ successfully prevents _suboptimal transfer_ and consistently improves expected robustness.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

Personalized Federated Fine-Tuning of Vision Foundation Models for Healthcare

Adam Tupper

2025-10-13

ArXiv (prépublication)

arxiv.org

A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

Ola Ahmad

Deep learning models operating in the image domain are vulnerable to small input perturbations. For years, robustness to such perturbations … (voir plus)was pursued by training models from scratch (i.e., with random initializations) using specialized loss objectives. Recently, robust fine-tuning has emerged as a more efficient alternative: instead of training from scratch, pretrained models are adapted to maximize predictive performance and robustness. To conduct robust fine-tuning, practitioners design an optimization strategy that includes the model update protocol (e.g., full or partial) and the specialized loss objective. Additional design choices include the architecture type and size, and the pretrained representation. These design choices affect robust generalization, which is the model's ability to maintain performance when exposed to new and unseen perturbations at test time. Understanding how these design choices influence generalization remains an open question with significant practical implications. In response, we present an empirical study spanning 6 datasets, 40 pretrained architectures, 2 specialized losses, and 3 adaptation protocols, yielding 1,440 training configurations and 7,200 robustness measurements across five perturbation types. To our knowledge, this is the most diverse and comprehensive benchmark of robust fine-tuning to date. While attention-based architectures and robust pretrained representations are increasingly popular, we find that convolutional neural networks pretrained in a supervised manner on large datasets often perform best. Our analysis both confirms and challenges prior design assumptions, highlighting promising research directions and offering practical guidance.

2025-09-28

NeurIPS.cc/2025/Workshop/Reliable_ML (publié)

High-order Component Attribution via Kolmogorov-Arnold Networks

Samy Mammeri

Component attribution methods provide insight into how parts of deep learning models, such as convolutional filters and attention heads, inf… (voir plus)luence model predictions. Despite their successes, existing attribution approaches typically assume component effects are additive and independent, neglecting complex interactions among components. Capturing these relations between components is crucial for a better mechanistic understanding of these models. In this work, we improve component attribution (COAR) by replacing the linear counterfactual estimator with a Kolmogorov–Arnold Network (KAN) surrogate fitted to example‑wise perturbation–response data. Then, a symbolic approximation of the learned KAN lets us compute mixed partial derivatives that captures and makes explicit high‑order component interactions that linear methods are missing. These symbolic expressions facilitate future integration with formal verification methods, enabling richer counterfactual analyses of internal model behavior. Preliminary results on standard image classification models demonstrate that our approach improves the accuracy of predicted counterfactuals and enable extraction of higher-order component interactions compared to linear attribution methods.

2025-09-28

NeurIPS.cc/2025/Workshop/MechInterp (poster)

Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling

Jonas Ngnawe

Maxime Heuillet

Sabyasachi Sahoo

Yann Batiste Pequignot

Frédéric Precioso

Fine-tuning pretrained models is the standard approach in current machine learning practice, but simultaneously achieving adversarial robust… (voir plus)ness to adversarial examples remains a challenge. Despite the abundance of non-robust pretrained models in open-source repositories, their use for Robust Fine-Tuning (RFT) remains understudied. This work aims to bridge this knowledge gap by systematically examining RFT from such models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub \emph{suboptimal transfer}. In fact, we find that fine-tuning using a robust objective impedes task alignment at the beginning of training and eventually prevents optimal transfer. To promote optimal transfer, we propose \emph{Epsilon-Scheduling}, a simple heuristic scheduling over perturbation strength. Additionally, we introduce \emph{expected robustness}, a metric that measures performance across a range of perturbations. Experiments on six pretrained models and five datasets show that \emph{Epsilon-Scheduling} prevents \emph{suboptimal transfer} and consistently improves the expected robustness.

2025-09-28

NeurIPS.cc/2025/Workshop/Reliable_ML (publié)

Conditional Adversarial Random Forest for Synthetic Electronic Health Record Generation

Cynthia García Ybarra

2025-09-21

NeurIPS.cc/2025/Workshop/WiML (publié)

Enhancing STED Microscopy via Fluorescence Lifetime Unmixing and Filtering in Two-Species SPLIT-STED

Andréanne Deschênes

Antoine Ollier

Marie Lafontaine

Albert Michaud-Gagnon

Jeffrey-Gabriel Steavan Santiague

Anthony Bilodeau

Paul De Koninck

Flavie Lavoie-Cardinal

2025-07-17

bioRxiv (prépublication)