Publications

How AI Is Reshaping Pricing Litigation
Maxime C. Cohen
2025-12-31
SSRN Electronic Journal (publié)
doi.org
Impact of an LLM-based Review Assistant in Practice: A Mixed Open-/Closed-source Case Study
Doriane Olewicki
Leuson Da Silva
Oussama Ben Sghaier
Suhaib Mujahid
Arezou Amini
Benjamin Mah
Marco Castelluccio
Sarra Habchi
Foutse Khomh
Bram Adams
2025-12-31
IEEE Transactions on Software Engineering (publié)
doi.org
In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior
Anaïs Berkes
Vincent Taboga
Donna Vakalis
David Rolnick
Yoshua Bengio
In-context reinforcement learning (ICRL) promises fast adaptation to unseen environments without parameter updates, but current methods eith… (voir plus)er cannot improve beyond the training distribution or require near-optimal data, limiting practical adoption. We introduce SPICE, a Bayesian ICRL method that learns a prior over Q-values via deep ensemble and updates this prior at test-time using in-context information through Bayesian updates. To recover from poor priors resulting from training on sub-optimal data, our online inference follows an Upper-Confidence Bound rule that favours exploration and adaptation. We prove that SPICE achieves regret-optimal behaviour in both stochastic bandits and finite-horizon MDPs, even when pretrained only on suboptimal trajectories. We validate these findings empirically across bandit and control benchmarks. SPICE achieves near-optimal decisions on unseen tasks, substantially reduces regret compared to prior ICRL and meta-RL approaches while rapidly adapting to unseen tasks and remaining robust under distribution shift.
2025-12-31
arXiv (prépublication)
doi.org
arxiv.org
Inference-time Physics Alignment of Video Generative Models with Latent World Models
Jianhao Yuan
Xiaofeng Zhang
Felix Friedrich
Nicolas Beltran-Velez
Melissa Hall
Reyhane Askari-Hemmat
Xiaochuang Han
Nicolas Ballas
Michal Drozdzal
Adriana Romero-Soriano
State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility… (voir plus). While some attribute this deficiency to insufficient physics understanding from pre-training, we find that the shortfall in physics plausibility also stems from suboptimal inference strategies. We therefore introduce WMReward and treat improving physics plausibility of video generation as an inference-time alignment problem. In particular, we leverage the strong physics prior of a latent world model (here, VJEPA-2) as a reward to search and steer multiple candidate denoising trajectories, enabling scaling test-time compute for better generation performance. Empirically, our approach substantially improves physics plausibility across image-conditioned, multiframe-conditioned, and text-conditioned generation settings, with validation from human preference study. Notably, on the challenging PhysicsIQ benchmark we achieve 62.00% final score, outperforming previous state of the art by 6.78%. Our work demonstrates the viability of using latent world models to improve physical plausibility of video generation, beyond this specific instantiation or parameterization.
2025-12-31
IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept (Highlight))
doi.org
arxiv.org
Integrating Generative and Experimental Platforms for Biomolecular Design
Chenghao Liu
Jarrid Rector-Brooks
Soojung Yang
Sidney Lisanza
Jacob Gershon
Lauren Hong
Pranam Chatterjee
Yoshua Bengio
Biomolecular design, through artificial engineering of proteins, ligands, nucleic acids, and cells, holds immense promise in addressing pres… (voir plus)sing medical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a disconnect exists with experimental biology: many ML research efforts prioritize static benchmark performance, potentially sidelining impactful biological applications. This workshop seeks to bridge this gap by bringing computationalists and experimentalists together, catalyzing a deeper interdisciplinary discourse. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and biological problems ready for ML. To attract high-quality and diverse research, we partnered with Nature Biotechnology for a special collection, and we created dedicated tracks for in-silico ML research and hybrid ML-experimental biology research. Our lineup features emerging leaders as speakers and renowned scientists as panelists, encapsulating a spectrum from high-throughput experimentation and computational biology to generative ML. To catalyze new collaborations, we will host a seed-grant competition for pairs of experimentalists and computationalists proposing fresh joint projects. To connect dry and wet lab practice, a wet-lab challenge sponsored by Adaptyv Bio will empirically evaluate protein design models. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology. This will be the third edition of this workshop following the previous versions of it we organized at ICLR 2024 and 2025.
2025-12-31
Workshop Proposals @ International Conference on Learning Representations (publié)
openreview.net
openreview.net
Interpreting Physics in Video World Models
Sonia Joseph
Quentin Garrido
Randall Balestriero
Matthew Kowal
Thomas Fel
Shahab Bakhtiari
Blake Richards
Mike Rabbat
A long-standing question in physical reasoning is whether video-based models need to rely on factorized representations of physical variable… (voir plus)s in order to make physically accurate predictions, or whether they can implicitly represent such variables in a distributed manner. While modern video world models achieve strong performance on intuitive physics benchmarks, it remains unclear which of these representational regimes they implement internally. Here, we present the first interpretability study to directly examine physical representations inside large-scale video encoders. Using layerwise probing, subspace geometry, patch-level decoding, and targeted attention ablations, we characterize where physical information becomes accessible and how it is organized within encoder-based video transformers. Across architectures, we identify a sharp intermediate-depth transition— which we call the \emph{Physics Emergence Zone}—at which physical variables become accessible. Physics-related representations peak shortly after this transition and degrade toward the output layers. Decomposing motion into explicit variables, we find that scalar quantities such as speed and acceleration are available from early layers onwards, whereas motion direction becomes accessible only at the Physics Emergence Zone. Notably, we find that direction is encoded through a high-dimensional population structure with circular geometry, requiring coordinated multi-feature intervention to control. These findings suggest that modern video models do not use factorized representations of physical variables like a classical physics engine. Instead, they use a distributed representation that is nonetheless sufficient for making physical predictions.
2025-12-31
International Conference on Machine Learning (Accept (regular))
doi.org
openreview.net
KILO-EKF: Koopman-Inspired Learned Observations Extended Kalman Filter
Zi Cong Guo
James Richard Forbes
Timothy D. Barfoot
We present the Koopman-Inspired Learned Observations Extended Kalman Filter (KILO-EKF), which combines a standard EKF prediction step with a… (voir plus) correction step based on a Koopman-inspired measurement model learned from data. By lifting measurements into a feature space where they are linear in the state, KILO-EKF enables flexible modeling of complex or poorly calibrated sensors while retaining the structure and efficiency of recursive filtering. The resulting linear-Gaussian measurement model is learned in closed form from groundtruth training data, without iterative optimization or reliance on an explicit parametric sensor model. At inference, KILO-EKF performs a standard EKF update using Jacobians obtained via the learned lifting. We validate the approach on a real-world quadrotor localization task using an IMU, ultra-wideband (UWB) sensors, and a downward-facing laser. We compare against multiple EKF baselines with varying levels of sensor calibration. KILO-EKF achieves better accuracy and consistency compared to data-calibrated baselines, and significantly outperforms EKFs that rely on imperfect geometric models, while maintaining real-time inference and fast training. These results demonstrate the effectiveness of Koopman-inspired measurement learning as a scalable alternative to traditional model-based calibration.
2025-12-31
arXiv (prépublication)
doi.org
arxiv.org
Large language models for electronic health records in pediatric and surgical care: a systematic review.
Carmel Daskalo
Waseem Abu-Ashour
Jean Marie Tshimula
Mohsen Amoei
Elena Guadagno
Dan Poenaru
2025-12-31
Journal of Pediatric Surgery (publié)
doi.org
Learning from Pairwise Preferences in Long-Term Decision Problems
Jonathan Colaço Carr
Prakash Panangaden
Doina Precup
Benjamin Van Roy
Agents that can beat or tie any other under a model of pairwise preference have strong guarantees for both user satisfaction and overall soc… (voir plus)ial welfare. However, searching for these agents in long-term decision problems is not computationally tractable with current approaches, which require the size of an agent's policy to increase with the problem length. We introduce the \textit{Markov decision contest}, a model of learning from general preferences in long-term (infinite-horizon) decision problems. Within this model, we prove that agents only need a stationary Markov policy in order to be optimal (that is, to beat or tie any agent with a history-dependent policy); that the problem of finding an optimal policy is in P; and that a simple iterative algorithm (which we call Hedged Policy Iteration) converges to an optimal policy at a sublinear rate. In a suite of high-dimensional experiments, we demonstrate that Hedged Policy Iteration scales well to function approximation. Lastly, we present a near approximation of Hedged Policy Iteration, called HPI-Clip, which both matches the performance of Proximal Policy Optimization on reward-based tasks while also outperforming it on tasks with non-transitive preferences. These results show that learning from pairwise preferences in long-term decision problems can be far more tractable than what is known from prior work.
2025-12-31
International Conference on Machine Learning (Accept (regular))
openreview.net
openreview.net
Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection
Shivam Chandhok
Qian Yang
Oscar Mañas
Kanishk Jain
Leonid Sigal
Aishwarya Agrawal
Instruction tuning has been central to the success of recent vision-language models (VLMs), but it remains expensive-requiring large-scale d… (voir plus)atasets, high-quality annotations, and large compute budgets. We propose PRioritized cOncept learninG via Relative Error-driven Sample Selection (PROGRESS), a data- and compute-efficient framework that enables VLMs to dynamically select what to learn next based on their evolving needs during training. At each stage, the model tracks its learning progress across skills and selects the most informative samples-those it has not already mastered and that are not too difficult to learn at the current stage of training. This strategy effectively controls skill acquisition and the order in which skills are learned. Specifically, we sample from skills showing the highest learning progress, prioritizing those with the most rapid improvement. Unlike prior methods, PROGRESS requires no upfront answer annotations, queries answers only on a need basis, avoids reliance on additional supervision from auxiliary VLMs, and does not require compute-heavy gradient computations for data selection. Experiments across multiple instruction-tuning datasets of varying scales demonstrate that PROGRESS consistently outperforms state-of-the-art baselines with much less data and supervision. Additionally, we show strong cross-architecture generalization and transferability to larger models, validating PROGRESS as a scalable solution for efficient learning.
2025-12-31
IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept (Poster))
doi.org
openreview.net
Leveraging Diversity for Privileged Multi-Teacher Knowledge Distillation for Facial Expression Recognition
Muhammad Haseeb Aslam
Marco Pedersoli
Alessandro L. Koerich
Eric Granger
2025-12-31
SSRN Electronic Journal (accepté)
doi.org
LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
Chuqin Geng
Ziyu Zhao
Zhaoyue Wang
Haolin Ye
Yuhe Jiang
Xujie Si
Existing rule-based explanations for Graph Neural Networks (GNNs) provide global interpretability but often optimize and assess fidelity in … (voir plus)an intermediate, uninterpretable concept space, overlooking the grounding quality of the final subgraph explanations for end users. This gap yields explanations that may appear faithful yet be unreliable in practice. To this end, we propose LogicXGNN, a post hoc framework that constructs logical rules over reliable predicates explicitly designed to capture the GNN's message-passing structure, thereby ensuring effective grounding. We further introduce data-grounded fidelity (
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net