Publications

Reply to comment on "medication-based mortality prediction in COPD using machine learning and conventional statistical methods".
Ana Paula Pena-Gralle
Amélie Forget
Yohann Moanahere Chiu
Marc-André Legault
M. Beauchesne
Lucie Blais
2025-12-31
International Journal of Medical Informatics (published)
doi.org
RetINaBox: A hands-on learning tool for experimental neuroscience
Brune Bettler
Flavia Arias Armas
Erica Cianfarano
Vanessa Bordonaro
Megan Liu
Matthew Loukine
Mingyu Wan
Aude Villemain
Blake Richards
Stuart Trenholm
An exciting aspect of neuroscience is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdle… (see more)s, the experiment and discovery component of neuroscience is generally lacking in classroom and outreach settings. To address this issue, here we introduce RetINaBox: a low-cost open-source electronic visual system simulator that provides users with a hands-on tool to discover how the visual system builds feature detectors. RetINaBox features an LED array for generating visual stimuli and a photodiode array that acts as a mosaic of model photoreceptors. Custom software on a Raspberry Pi computer reads out responses from model photoreceptors and allows users to control the polarity and delay of the signal transfer from model photoreceptors to model retinal ganglion cells. Interactive lesson plans are provided, guiding users to discover different types of visual feature detectors—including ON/OFF, center-surround, orientation selective, and direction selective receptive fields—as well as their underlying circuit computations.
2025-12-31
eNeuro (published)
doi.org
Revisiting Age of Acquisition in Curriculum Learning: Disentangling Lexical Features and Semantic Structure
Ian Gifford
Aaron Shah
Catherine Chen
Taimaa Kassab Bachi
Eva Portelance
Previous work has found that ordering training data by children’s Age of Acquisition (AoA) for words increases the stability of distributi… (see more)onal word embeddings, suggesting that early-learned words play a privileged role in shaping semantic structure. In this study, we determine whether AoA itself drives these effects, or whether they emerge from correlated lexical factors such as frequency, concreteness, and phonological complexity. Using incremental Word2Vec training, we construct curricula ordered by AoA and by individual lexical features, while systematically controlling for vocabulary growth and deterministic ordering effects. We show that AoA-ordered curricula produce greater early-phase stability than shuffled baselines, even under controlled exposure conditions. We find that the advantage observed with AoA can be largely explained by correlated factors like overall word frequency. Despite limited gains on general similarity benchmarks, AoA-ordered embeddings outperform shuffled embeddings on a proxy domain-specific task: predicting human AoA norms. This advantage persists after debiasing timestamp effects, implying that AoA curricula induce developmentally meaningful semantic structure.
2025-12-31
Conference on Computational Natural Language Learning (published)
doi.org
openreview.net
Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
Jonas Ngnawe
Maxime Heuillet
Sabyasachi Sahoo
Yann Pequignot
Ola Ahmad
Audrey Durand
Frédéric Precioso
Christian Gagné
Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims… (see more) 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))
doi.org
openreview.net
Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute
Kieran Didi
Zuobai Zhang
Guoqing Zhou
Danny Reidenbach
Zhonglin Cao
Sooyoung Cha
Tomas Geffner
Christian Dallago
Jian Tang
Michael Bronstein
Martin Steinegger
Emine Kucukbenli
Arash Vahdat
Karsten Kreis
Protein interaction modeling is central to protein design, which has been transformed by machine learning with broad applications in drug di… (see more)scovery and beyond. In this landscape, structure-based de novo binder design is most often cast as either conditional generative modeling or sequence optimization via structure predictors ("hallucination"). We argue that this is a false dichotomy and propose Complexa, a novel fully atomistic binder generation method unifying both paradigms. We extend recent flow-based latent protein generation architecture and leverage the domain-domain interactions of monomeric computationally predicted protein structures to construct Teddymer, a new large-scale dataset of synthetic binder-target pairs for pretraining. Combined with high-quality experimental multimers, this enables training a strong base model. We then perform inference-time optimization with this generative prior, unifying the strengths of previously distinct generative and hallucination methods. Complexa sets a new state of the art in computational binder design benchmarks: it delivers markedly higher in-silico success rates than existing generative approaches, and our novel test-time optimization strategies greatly outperform previous hallucination methods under normalized compute budgets. We further demonstrate explicit interface hydrogen bond optimization, fold class-guided binder generation, and extensions to small molecule targets and enzyme design tasks, again surpassing prior methods. Code, models and new data will be publicly released.
2025-12-31
International Conference on Learning Representations (Accept (Oral))
openreview.net
openreview.net
SelvaBox: A high‑resolution dataset for tropical tree crown detection
Hugo Baudchon
Arthur Ouaknine
Martin Weiss
Mélisande Teng
Thomas Walla
Antoine Caron-Guay
Christopher Pal
Étienne Laliberté
Detecting individual tree crowns in tropical forests is essential to study these complex and crucial ecosystems impacted by human interventi… (see more)ons and climate change. However, tropical crowns vary widely in size, structure, and pattern and are largely overlapping and intertwined, requiring advanced remote sensing methods applied to high-resolution imagery. Despite growing interest in tropical tree crown detection, annotated datasets remain scarce, hindering robust model development. We introduce SelvaBox, the largest open‑access dataset for tropical tree crown detection in high-resolution drone imagery. It spans three countries and contains more than
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
Semi-Supervised Learning for Time-Series Segmentation in Equine Gait Event Detection
Mahaut Gerard
Guillaume Dubois
Sandrine Hanne-Poujade
Camille Hébert
Henry Château
Neila Mezghani
International audience
2025-12-31
IEEE Access (published)
doi.org
Set Representation Auxiliary Learning with Adversarial Encoding Perturbation and Optimization
Yankai Chen
Xinni Zhang
Henry Peng Zou
Bowei He
Yangning Li
Philip S. Yu
Irwin King
Xue Liu
Sets are a fundamental data structure, and learning their vectorized representations is crucial for many computational problems. Existing me… (see more)thods typically focus on intra-set properties such as permutation invariance and cardinality independence. While effective at preserving basic intra-set semantics, these approaches may be insufficient in explicitly modeling inter-set correlations, which are critical for tasks requiring fine-grained comparisons between sets. In this work, we propose SRAL, a Set Representation Auxiliary Learning framework for capturing inter-set correlations that is compatible with various downstream tasks. SRAL conceptualizes sets as high-dimensional distributions and leverages the 2-Sliced-Wasserstein distance to derive their distributional discrepancies into set representation encoding. More importantly, we introduce a novel adversarial auxiliary learning scheme. Instead of manipulating the input data, our method perturbs the set encoding process itself and compels the model to be robust against worst-case perturbations through a min-max optimization. Our theoretical analysis shows that this objective, in expectation, directly optimizes for the set-wise Wasserstein distances, forcing the model to learn highly discriminative representations. Comprehensive evaluations across four downstream tasks examine SRAL’s performance relative to baseline methods, showing consistent effectiveness in both inter-set relation-sensitive retrieval and intra-set information-oriented processing tasks.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
Kaustubh Mani
Yann Pequignot
Vincent Mai
Liam Paull
Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach s… (see more)afe exploration through the lens of epistemic uncertainty, where the actor’s sensitivity to parameter perturbations serves as a practical proxy for regions of high uncertainty. We propose Sharpness-Aware Policy Optimization (SHAPO), a sharpness-aware policy update rule that evaluates gradients at perturbed parameters, making policy updates pessimistic with respect to the actor’s epistemic uncertainty. Analytically we show that this adjustment implicitly reweighs policy gradients, amplifying the influence of rare unsafe actions while tempering contributions from already safe ones, thereby biasing learning toward conservative behavior in under-explored regions. Across several continuous-control tasks, our method consistently improves both safety and task performance over existing baselines, significantly expanding their Pareto frontiers.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
Spatial CAPTCHA: Generatively Benchmarking Spatial Reasoning for Human-Machine Differentiation
Arina Kharlamova
Bowei He
Chen Ma
Xue Liu
Online services rely on CAPTCHAs as a first line of defense against automated abuse, yet recent advances in multi-modal large language model… (see more)s (MLLMs) have eroded the effectiveness of conventional designs that focus on text recognition or 2D image understanding. To address this challenge, we present **Spatial CAPTCHA**, a novel human-verification framework that leverages fundamental differences in spatial reasoning between humans and MLLMs. Unlike existing CAPTCHAs that rely on low-level perception tasks vulnerable to modern AI, Spatial CAPTCHA generates dynamic questions requiring geometric reasoning, perspective-taking, occlusion handling, and mental rotation—skills intuitive for humans but difficult for current AI systems. The system employs a procedural generation pipeline with constraint-based difficulty control, automated correctness verification, and human-in-the-loop validation to ensure scalability, robustness, and adaptability. Evaluation on a corresponding benchmark, **Spatial-CAPTCHA-Bench**, demonstrates that humans vastly outperform 10 state-of-the-art MLLMs, with the best model achieving only 31.0\% Pass@1 accuracy. Result comparison with Google reCAPTCHA further confirms the effectiveness of Spatial CAPTCHA as both a security mechanism and a diagnostic tool for spatial reasoning in AI.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Spatial pattern regression for meteorological fields interpolation
Vihotogb eacute Houssou
Julie Carreau
2025-12-31
SSRN Electronic Journal (accepted)
doi.org
Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation
Zhichao Wu
Junyin Ye
Zhilong Zhang
Yihao Sun
Haoxin Lin
Jiaheng Luo
Haoxiang Ren
Lei Yuan
Yang Yu
While current embodied policies exhibit remarkable manipulation skills, their execution remains unsatisfactorily slow as they inherit the ta… (see more)rdy pacing of human demonstrations. Existing acceleration methods typically require policy retraining or costly online interactions, limiting their scalability for large-scale foundation models. In this paper, we propose **S**peed**u**p **P**atch (**SuP**), a lightweight, policy-agnostic framework that enables **plug-and-play acceleration** using solely offline data. SuP introduces an external scheduler that adaptively downsamples action chunks provided by embodied policies to eliminate redundancies. Specifically, we formalize the optimization of our scheduler as a Constrained Markov Decision Process (CMDP) aimed at maximizing efficiency without compromising task performance. Since direct success evaluation is infeasible in offline settings, SuP introduces **World Model based state deviation** as a surrogate metric to enforce safety constraints. By leveraging a learned world model as a virtual evaluator to predict counterfactual trajectories, the scheduler can be optimized via offline reinforcement learning. Empirical results on simulation benchmarks (Libero, Bigym) and real-world tasks validate that SuP achieves an overall
2025-12-31
International Conference on Machine Learning (Accept (regular))
doi.org
openreview.net