Publications

Amortized In-Context Bayesian Posterior Estimation
Sarthak Mittal
N. L. Bracher
Guillaume Lajoie
Priyank Jaini
Marcus Brubaker
Bayesian inference provides a natural way of incorporating prior beliefs and assigning a probability measure to the space of hypotheses. Cur… (voir plus)rent solutions rely on iterative routines like Markov Chain Monte Carlo (MCMC) sampling and Variational Inference (VI), which need to be re-run whenever new observations are available. Amortization, through conditional estimation, is a viable strategy to alleviate such difficulties and has been the guiding principle behind simulation-based inference, neural processes and in-context methods using pre-trained models. In this work, we conduct a thorough comparative analysis of amortized in-context Bayesian posterior estimation methods from the lens of different optimization objectives and architectural choices. Such methods train an amortized estimator to perform posterior parameter inference by conditioning on a set of data examples passed as context to a sequence model such as a transformer. In contrast to language models, we leverage permutation invariant architectures as the true posterior is invariant to the ordering of context examples. Our empirical study includes generalization to out-of-distribution tasks, cases where the assumed underlying model is misspecified, and transfer from simulated to real problems. Subsequently, it highlights the superiority of the reverse KL estimator for predictive problems, especially when combined with the transformer architecture and normalizing flows.
2025-02-09
ArXiv (prépublication)
doi.org
arxiv.org
FairDropout: Using Example-Tied Dropout to Enhance Generalization of Minority Groups
Geraldin Nanfack
Eugene Belilovsky
2025-02-09
ArXiv (prépublication)
doi.org
arxiv.org
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Eric Aubinais
Philippe Formont
Pablo Piantanida
Elisabeth Gassiat
Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance l… (voir plus)evels comparable to the original models. In this work, we investigate the impact of quantization procedures on the privacy of data-driven models, specifically focusing on their vulnerability to membership inference attacks. We derive an asymptotic theoretical analysis of Membership Inference Security (MIS), characterizing the privacy implications of quantized algorithm weights against the most powerful (and possibly unknown) attacks. Building on these theoretical insights, we propose a novel methodology to empirically assess and rank the privacy levels of various quantization procedures. Using synthetic datasets, we demonstrate the effectiveness of our approach in assessing the MIS of different quantizers. Furthermore, we explore the trade-off between privacy and performance using real-world data and models in the context of molecular modeling.
2025-02-09
ArXiv (prépublication)
doi.org
arxiv.org
Overcoming Political Upheaval to Deliver Pediatric Surgical Care in Afghanistan: A Prospective Analysis of the First 1000 Procedures.
Dunya Moghul
Phillip J Hsu
Emma Bryce
Yalda Obaidy
Zane Hellman
Ajmal Sherzad
Dan Poenaru
Maija Cheung
BACKGROUND Pediatric surgical care is limited in Afghanistan. Few details are known about the state of pediatric surgery in Afghanistan. We … (voir plus)explore the impact of a newly installed pediatric operating room by a children's charity on the provision of pediatric surgery in Afghanistan. STUDY DESIGN Following the opening in March 2023 of the new KidsOR operating room at Ataturk Hospital in Kabul, Afghanistan, perioperative clinical data was prospectively collected until December 2023. All children (age 14 years) undergoing surgical procedures were included in a REDCap database, and descriptive analyses were performed. RESULTS 1,014 opera
2025-02-09
Journal of the American College of Surgeons (publié)
doi.org
Using Image-based Al for insect monitoring and conservation - InsectAI COST Action
Tom August
Mario V Balzan
Paul Bodesheim
Gunnar Brehm
Lisette Cantú-Salazar
Sílvia Castro
Joseph Chipperfield
Guillaume Ghisbain
Alba Gomez-Segura
Jérémie Goulnik
Quentin Groom
Laurens Hogeweg
Chantal Huijbers
Andreas Kamilaris
Karolis Kazlauskis
Wouter Koch
Dimitri Korsch
João Loureiro
Youri Martin
Angeliki F Martinou … (voir 27 de plus)
Kent McFarland
Xavier Mestdagh
Denis Michez
Charlie Outhwaite
Luca Pegoraro
Nadja Pernat
Lars B. Pettersson
Pavel Pipek
Cristina Preda
David Rolnick
Tobias Roth
David B Roy
Helen Roy
Veljo Runnel
Martina Sasic
Dmitry Schigel
Julie Koch Sheard
Cecilie Svenningsen
Heliana Teixeira
Nicolas Titeux
Thomas Tscheulin
Elli Tzirkalli
Marijn van der Velde
Roel van Klink
Nicolas J Vereecken
Sarah Vray
Toke Thomas Høye
The InsectAI COST action will support insect monitoring and conservation at the national and continental scale in order to understand and co… (voir plus)unteract widespread insect declines. The Action will bring together a critical mass of researchers and stakeholders in image-based insect AI technologies to direct and drive the research agenda, build research capacity across Europe and support innovation and application. There is mounting evidence that populations of insects around the world are in sharp decline. Understanding trends in species and their drivers is key to knowing the size of the challenge, its causes and how to address it. To identify solutions that lead to sustainable biodiversity alongside economic prosperity, insect monitoring should be efficient and provide standardised and frequently updated status indicators to guide conservation actions. The EU Biodiversity Strategy 2030 identifies the critical challenge of delivering standardised information about the state of nature and image-based insect AI can contribute to this. Specifically, the EU Nature Restoration Law will likely set binding targets for the high resolution data that cameras can provide. Thus, outputs of the Action will contribute directly to EU policies implementation, where biodiversity monitoring is considered a key component. The InsectAI COST Action will organise workshops, conferences, short-term scientific missions, hackathons, design-sprints and much more, across four Working Groups. These groups will address how image-based insect AI technologies can best address Societal Needs, support innovation in Image Collection hardware, create standardised approaches for Image Processing and develop novel Data Analysis and Integration methods for turning data into actionable insights.
2025-02-09
Research Ideas and Outcomes (publié)
doi.org
Improving Patient Safety Culture in Conflict-Affected Zones: A Cross-Sectional Survey of North Kivu Surgical Personnel in the Democratic Republic of the Congo.
Jacques Fadhili Bake
Claude Kasereka Masumbuko
Zacharie Tsongo Kibendelwa
Georges Bushu Lubuto
Jean‐Claude Mafuta Kyembwa
Esaie Kasereka Nzala
Papy Waleyirwe Kakule
Clovis Bwami Akumbi
Jean Zanga Kitutu
Tresor Basubi Wakilongo
Theophile Kubuya Hangi
Wilson Katembo Kwiraviwe
Benjamin Musemakweli
Beate Tshikudju Bahati
Steve Kisembo Bakabona
Dan Poenaru
BACKGROUND Patient safety culture significantly impacts outcomes in surgery, where preventable errors can occur. This study assessed patient… (voir plus) safety culture and its determinants in operating rooms across North Kivu, a conflict-affected province in the eastern Democratic Republic of the Congo (DRC). METHODS A descriptive multicenter cross-sectional study was conducted from July to September 2024 in five urban and six rural hospitals. The French version of the Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire was administered to 328 operating room healthcare professionals. RESULTS The response rate was 78% (256 completed surveys). Urban hospitals accounted for 55.5% of respondents, who were 73.4% male and 62.5% under the age of 40. The overall composite score for patient safety culture was 63.2%. Teamwork (81.1%) and management support for patient safety (77.7%) received the highest positive responses, whereas error reporting (39.9%) and patient safety event reporting (50%) scored lower. Half (49.6%) of the respondents rated patient safety as excellent or very good. There were no significant differences in overall mean composite scores between urban and rural hospitals (p = 0.677) and between medical and paramedical staff (p = 0.694). CONCLUSIONS The patient safety culture rating in North Kivu falls below international standards, highlighting an urgent need for improvement, particularly in error response and event reporting. Developing a tailored patient safety bundle for the region is essential to enhance overall health outcomes.
2025-02-06
World Journal of Surgery (publié)
doi.org
Agency Is Frame-Dependent
David Abel
Andre Barreto
Michael Bowling
Will Dabney
Shi Dong
Steven Stenberg Hansen
Anna Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (voir plus), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.
2025-02-05
ArXiv (prépublication)
doi.org
arxiv.org
Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
Gabriel Missael Barco
Alexandre Adam
Connor Stone
Yashar Hezaveh
Laurence Perreault-Levasseur
Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-… (voir plus)level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. However, in many astrophysical applications, it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. In these cases, corrupted data or a surrogate, e.g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors. This, in turn, can bias the inferred posteriors in ways that are difficult to quantify, which limits the potential applicability of these models in real-world scenarios. In this work, we propose addressing this issue by iteratively updating the population-level distributions by retraining the model with posterior samples from different sets of observations and showcase the potential of this method on the problem of background image reconstruction in strong gravitational lensing when score-based models are used as data-driven priors. We show that starting from a misspecified prior distribution, the updated distribution becomes progressively closer to the underlying population-level distribution, and the resulting posterior samples exhibit reduced bias after several updates.
2025-02-05
The Astrophysical Journal (publié)
doi.org
arxiv.org
Rapid restoration of potent neutralization activity against the latest Omicron variant JN.1 via AI rational design and antibody engineering
Yunji Liao
Hang Ma
Zhenyu Wang
Shusheng Wang
Yang He
Yunsong Chang
Huifang Zong
Haoneng Tang
Lei Wang
Yong Ke
Huiyu Cai
Ping Li
Jian Tang
Yunsheng Yuan
Aleksandra Drelich
Bi-Hung Peng
Jason Hsu
Vivian Tat
Chien-Te K. Tseng
Jingjing Song … (voir 22 de plus)
Yunsheng Yuan
Mingyuan Wu
Junjun Liu
Yali Yue
Xiaoju Zhang
Ziqi Wang
Yang He
Jing Li
Xiaodan Ni
Hongshi Li
Yuning Xiang
Yanlin Bian
Baohong Zhang
Haiyang Yin
Dimiter S. Dimitrov
John Gilly
Lei Han
Hua Jiang
Yueqing Xie
Jianwei Zhu
Yueqing Xie
Jianwei Zhu
The rapid evolution of the viral genome has led to the continual generation of new variants of SARS-CoV-2. Developing antibody drugs with br… (voir plus)oad-spectrum and high efficiency is a long-term task. It is promising but challenging to develop therapeutic neutralizing antibodies (nAbs) through in vitro evolution based on antigen–antibody binding interactions. From an early B cell antibody repertoire, we isolated antibody 8G3 that retains its nonregressive neutralizing activity against Omicron BA.1 and various other strains in vitro. 8G3 protected ACE2 transgenic mice from BA.1 and WA1/2020 virus infection without adverse clinical manifestations and completely cleared viral load in the lungs. Similar to most IGHV3–53 antibodies, the binding sites of 8G3 and ACE2 largely overlap, enabling competition with ACE2 for binding to RBD. By comprehensively considering the binding free energy changes of the antigen–antibody complexes, the biological environment of their interactions, and the evolutionary direction of the antibodies, we were able to select 50 mutants. Among them, 11 were validated by experiments showing better neutralizing activities. Further, a combination of four mutations were identified in 8G3 that increased its neutralization potency against JN.1, the latest Omicron mutant, by approximately 1,500-fold, and one of the mutations led to an improvement in activity against multiple variants to a certain extent. Together, we established a procedure of rapid selection of neutralizing antibodies with potent SARS-CoV-2 neutralization activity. Our results provide a reference for engineering neutralizing antibodies against future SARS-CoV-2 variants and even other pandemic viruses.
2025-02-04
Proceedings of the National Academy of Sciences (publié)
doi.org
Rapid restoration of potent neutralization activity against the latest Omicron variant JN.1 via AI rational design and antibody engineering
Yunji Liao
Hang Ma
Zhenyu Wang
Shusheng Wang
Yang He
Yunsong Chang
Huifang Zong
Haoneng Tang
Lei Wang
Yong Ke
Huiyu Cai
Ping Li
Jian Tang
Yunsheng Yuan
Aleksandra Drelich
Bi-Hung Peng
Jason Hsu
Vivian Tat
Chien-Te K. Tseng
Jingjing Song … (voir 20 de plus)
Yunsheng Yuan
Mingyuan Wu
Junjun Liu
Yali Yue
Xiaoju Zhang
Ziqi Wang
Yang He
Jing Li
Xiaodan Ni
Hongshi Li
Yuning Xiang
Yanlin Bian
Baohong Zhang
Haiyang Yin
Dimiter S. Dimitrov
John Gilly
Lei Han
Hua Jiang
Yueqing Xie
Jianwei Zhu
Owing to the ongoing mutation of SARS-CoV-2, the vast majority of therapeutic antibodies developed in the early stages have lost their neutr… (voir plus)alizing effects. Here, we have developed neutralizing antibodies, including 8G3 isolated from patients infected with the wild-type SARS-CoV-2 and its mutants from computational rational design. Following the mutations of 8G3 through computational technology, the neutralizing activity of the antibody was enhanced by approximately 1,500-fold. Our experimental results offer a case study for the optimization of neutralizing antibodies against SARS-CoV-2 guided by computational technology.
2025-02-04
Proceedings of the National Academy of Sciences (publié)
doi.org
Temporally-Consistent Surface Reconstruction using Metrically-Consistent\n Atlases
Jan Bednařík
Noam Aigerman
Vladimir G. Kim
Siddhartha Chaudhuri
Shaifali Parashar
Mathieu Salzmann
Pascal Fua
We propose a method for unsupervised reconstruction of a\ntemporally-consistent sequence of surfaces from a sequence of time-evolving\npoint… (voir plus) clouds. It yields dense and semantically meaningful correspondences\nbetween frames. We represent the reconstructed surfaces as atlases computed by\na neural network, which enables us to establish correspondences between frames.\nThe key to making these correspondences semantically meaningful is to guarantee\nthat the metric tensors computed at corresponding points are as similar as\npossible. We have devised an optimization strategy that makes our method robust\nto noise and global motions, without a priori correspondences or pre-alignment\nsteps. As a result, our approach outperforms state-of-the-art ones on several\nchallenging datasets. The code is available at\nhttps://github.com/bednarikjan/temporally_coherent_surface_reconstruction.\n
2025-02-03
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
arxiv.org
Bridging Causality, Individual Fairness, and Adversarial Robustness in the Absence of Structural Causal Model
Ahmad Reza Ehyaei
Golnoosh Farnadi
Samira Samadi
Despite the essential need for comprehensive considerations in responsible AI, factors such as robustness, fairness, and causality are often… (voir plus) studied in isolation. Adversarial perturbation, used to identify vulnerabilities in models, and individual fairness, aiming for equitable treatment of similar individuals, despite initial differences, both depend on metrics to generate comparable input data instances. Previous attempts to define such joint metrics often lack general assumptions about data and were unable to reflect counterfactual proximity. To address this, our paper introduces a \emph{causal fair metric} formulated based on causal structures encompassing sensitive attributes and protected causal perturbation. To enhance the practicality of our metric, we propose metric learning as a method for metric estimation and deployment in real-world problems in the absence of structural causal models. We also demonstrate the applications of the causal fair metric in classifiers. Empirical evaluation of real-world and synthetic datasets illustrates the effectiveness of our proposed metric in achieving an accurate classifier with fairness, resilience to adversarial perturbations, and a nuanced understanding of causal relationships.
2025-02-02
TMLR (accepté)
openreview.net
openreview.net