Publications

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… (voir plus)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

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… (voir plus)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

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… (voir plus)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 (accepté)

doi.org

Spinal cord versus brain imaging biomarkers of multiple sclerosis trajectory combining 7T and 3T MRI

Alessandro Miscioscia

Constantina A Treaba

Elena Barbuti

Valeria T Barletta

Jacob A Sloane

Eric C Klawiter

Julien Cohen-Adad

Paolo Gallo

Patrizia Pantano

Caterina Mainero

Abstract In multiple sclerosis, different types of lesions and their localization can have varying effects on clinical disability and diseas… (voir plus)e progression. Ultra-high field 7-Tesla MRI improves the visualization of cortical, especially subpial, lesions and of white matter lesions with a paramagnetic rim that are associated with smoldering inflammation. Spinal cord atrophy is also a critical determinant of clinical disability in multiple sclerosis, but its importance relative to paramagnetic rim and cortical lesions in predicting neurological disability and its progression remains unclear. In this longitudinal study, we aimed to identify the most relevant predictors of both the baseline Expanded Disability Status Scale status and 4-year progression independent of relapse activity in a heterogeneous multiple sclerosis cohort. One-hundred-twelve patients (83 relapsing-remitting and 29 secondary progressive; mean age 42.3 years, mean disease duration 9.8 years) underwent 7-Tesla T2* susceptibility-weighted images to segment paramagnetic rim lesions, non-rim white matter lesions and cortical lesions; 3-Tesla T1-weighted brain MRI images extended to the C2-C3 spinal cord were employed to obtain brain volumes and the spinal cord C2-C3 cross-sectional area using FreeSurfer and Spinal Cord Toolbox. Clinical disability was assessed through the Expanded Disability Status Scale at baseline and, in 97/112 patients (86.6%), after a mean follow-up of 4.0 years. The association between imaging metrics and clinical outcome was evaluated using correlations and regression models, corrected for age, sex, treatment class and clinical follow-up time. The main predictors of baseline Expanded Disability Status Scale were cortical lesion (β = 2.9 × 10−4, P = 0.001), non-rim white matter lesion (β = 1.2 × 10−4, P 0.001) volumes, brain white matter volume (β = −15.68, P = 0.017) and C2-C3 cross-sectional area (β = −0.68, P = 0.003). At follow-up, 23/97 patients (24%) experienced progression independent of relapse activity. Progression independent of relapse activity was associated with paramagnetic rim lesion volume (odds ratio = 1.0006 per mm³ increase, P = 0.030), cortical lesion volume (odds ratio = 1.0005 per mm³ increase, P = 0.011) and brain white matter volume (odds ratio = 0.97 × 10−20, P 0.001). However, a stepwise logistic regression model assessing clinical, lesion and atrophy variables identified cortical lesion volume as the strongest independent predictor of progression independent of relapse activity (odds ratio = 1.0006 per mm³ increase, P = 0.005). In multiple sclerosis, different imaging biomarkers contribute differently to current disability and progression independent of relapse activity. Spinal cord atrophy mainly explains the current Expanded Disability Status Scale, while brain white matter atrophy and paramagnetic rim lesions provide additional insights into future disability trajectory. Among all markers, cortical lesions emerged as the main driver for progression independent of relapse activity.

2025-12-31

Brain Communications (publié)

doi.org

Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning

Nilaksh

In streaming Reinforcement Learning (RL), transitions are observed and discarded immediately after a single update. While this minimizes res… (voir plus)ource usage for on-device applications, it makes agents notoriously sample-inefficient, since value-based losses alone struggle to extract meaningful representations from transient data. We propose extending Self-Predictive Representations (SPR) to the streaming pipeline to maximize the utility of every observed frame. However, due to the highly correlated samples induced by the streaming regime, naively applying this auxiliary loss results in training instabilities. Thus, we introduce orthogonal gradient updates relative to the momentum target and resolve gradient conflicts arising from streaming-specific optimizers. Validated across the Atari, MinAtar, and Octax suites, our approach systematically outperforms existing streaming baselines. Latent-space analysis, including t-SNE visualizations and effective-rank measurements, confirms that our method learns significantly richer representations, bridging the performance gap caused by the absence of a replay buffer, while remaining efficient enough to train on just a few CPU cores.

2025-12-31

International Conference on Machine Learning (Accept (regular))

doi.org

openreview.net

SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning

Riyasat Ohib

Bishal Thapaliya

Gintare Karolina Dziugaite

Jingyu Liu 0001

Vince D. Calhoun

Sergey Plis

In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient commu… (voir plus)nication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each round between the clients and the server. On standard benchmarks including CIFAR-10, CIFAR-100, and Tiny-ImageNet, SSFL consistently improves the accuracy sparsity trade off, achieving more than 20\% relative error reduction on CIFAR-10 compared to the strongest sparse baseline, while reducing communication costs by

2025-12-31

Trans. Mach. Learn. Res. (publié)

arxiv.org

Stable Deep Reinforcement Learning via Isotropic Gaussian Representations

Johan Obando-Ceron

Deep reinforcement learning systems often suffer from unstable training dynamics due to non-stationarity, where learning objectives and data… (voir plus) distributions evolve over time. We show that under non-stationary targets, isotropic Gaussian embeddings are provably advantageous. In particular, they induce stable tracking of time-varying targets for linear readouts, achieve maximal entropy under a fixed variance budget, and encourage a balanced use of all representational dimensions--all of which enable agents to be more adaptive and stable. Building on this insight, we propose the use of Sketched Isotropic Gaussian Regularization for shaping representations toward an isotropic Gaussian distribution during training. We demonstrate empirically, over a variety of domains, that this simple and computationally inexpensive method improves performance under non-stationarity while reducing representation collapse, neuron dormancy, and training instability.

2025-12-31

International Conference on Machine Learning (Accept (spotlight))

doi.org

openreview.net

Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?

Michael Cohen

Joumana Ghosn

Adam Oberman

Jesse Richardson

Oliver Richardson

Marc-Antoine Rondeau

Pierre-Luc St-Charles

David Williams-King

The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue go… (voir plus)als across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.

2025-12-31

Zenodo (publié)

doi.org

arxiv.org

Supervised Multimodal Model for Plasma Spray Diagnostics and Spray Health Monitoring

Cormac Cureton

Abhijeet Praveen

Sareh Soleimani

Aman Sidhu

Cristian Cojocaru

Kintak Raymond Yu

Narges Armanfard

2025-12-31

SSRN Electronic Journal (accepté)

doi.org

Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization

Yonghan Yang

Bowei He

Can Chen

Xue Liu

Offline black-box optimization aims to discover novel designs with high property scores using only a static dataset, a task fundamentally ch… (voir plus)allenged by the out-of-distribution (OOD) extrapolation problem. Existing approaches typically bifurcate into inverse methods, which struggle with the ill-posed nature of mapping scores to designs, and forward methods, which often lack the distributional expressivity to quantify uncertainty effectively. In this work, we propose \textbf{SPADE} (\textbf{S}upport-\textbf{P}roximity \textbf{A}ugmented \textbf{D}iffusion \textbf{E}stimation), a novel framework that reimagines forward surrogate modeling through the lens of conditional generative modeling. SPADE models the forward likelihood

2025-12-31

International Conference on Machine Learning (Accept (regular))

doi.org

openreview.net

Suspected Biliary Atresia in Brazil: Impact of Regional Healthcare Variations on Diagnostic Timeliness

Luiza Telles

Paulo Henrique Moreira Melo

Ana Maria Bicudo Diniz

Gabriele Lech

Ayla Gerk

Lauren Kratky

Dan Poenaru

David P. Mooney

Joaquim Bustorff-Silva

2025-12-31

Journal of Pediatric Surgery Open (publié)

doi.org

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La plateforme Mila Ventures

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