Publications

Integrating Generative and Experimental Platforms for Biomolecular Design
Cheng-Hao Liu
Jarrid Rector-Brooks
Soojung Yang
Sidney L Lisanza
Francesca-Zhoufan Li
Hannes Stärk
Jacob Gershon
Lauren Hong
Pranam Chatterjee
Tommi Jaakkola
Regina Barzilay
David Baker
Frances H. Arnold
Yoshua Bengio
Biomolecular design, through artificial engineering of proteins, ligands, and nucleic acids, holds immense promise in addressing pressing me… (see more)dical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a palpable 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. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology.
2024-12-31
ICLR.cc/2025/Workshop_Proposals (published)
openreview.net
openreview.net
International AI Safety Report
Yoshua Bengio
Bronwyn Fox
André Carlos Ponce de Leon Ferreira de Carvalho
Mona Nemer
Raquel Pezoa Rivera
Yi Zeng
Juha Heikkilä
Guillaume Avrin
Antonio Krüger
Balaraman Ravindran
Hammam Riza
Ciarán Seoighe
Ziv Katzir
Andrea Monti
Hiroaki Kitano
Nusu Mwamanzi
Fahad Albalawi
José Ramón López Portillo
Haroon Sheikh
Gill Jolly … (see 86 more)
Olubunmi Ajala
Jerry Sheehan
Dominic Vincent Ligot
Kyoung Mu Lee
Crystal Rugege
Denise Wong
Nuria Oliver
Christian Busch
Ahmet Halit Hatip
Oleksii Molchanovskyi
Marwan Alserkal
Chris Johnson
Amandeep Singh Gill
Saif M. Khan
Sören Mindermann
Daniel Privitera
Tamay Besiroglu
Rishi Bommasani
Stephen Casper
Yejin Choi
Philip Fox
Ben Garfinkel
Danielle Goldfarb
Hoda Heidari
Anson Ho
Sayash Kapoor
Leila Khalatbari
Shayne Longpre
Sam Manning
Vasilios Mavroudis
Mantas Mazeika
Julian Michael
Jessica Newman
Kwan Yee Ng
Chinasa T. Okolo
Deborah Raji
Girish Sastry
Elizabeth Seger
Theodora Skeadas
Tobin South
Daron Acemoglu
Olubayo Adekanmbi
David Dalrymple
Thomas G. Dietterich
Edward W. Felten
Pascale Fung
Pierre-Olivier Gourinchas
Fredrik Heintz
Geoffrey Hinton
Nick Jennings
Andreas Krause
Susan Leavy
Percy Liang
Teresa Ludermir
Vidushi Marda
Emma Strubell
Florian Tramèr
Lucia Velasco
Nicole Wheeler
Helen Margetts
John McDermid
Jane Munga
Arvind Narayanan
Alondra Nelson
Clara Neppel
Alice Oh
Gopal Ramchurn
Stuart Russell
Marietje Schaake
Bernhard Schölkopf
Dawn Song
Alvaro Soto
Lee Tiedrich
Gael Varoquaux
Andrew Yao
Ya-Qin Zhang
Baran Acar
Ben Clifford
Lambrini Das
Claire Dennis
Freya Hempleman
Hannah Merchant
Rian Overy
Ben Snodin
Jonathan Barry
Benjamin Prud’homme
The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced… (see more) AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.
2024-12-31
arXiv (preprint)
doi.org
arxiv.org
Investigating Generalization Behaviours of Generative Flow Networks
Lazar Atanackovic
Emmanuel Bengio
Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spa… (see more)ces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favourable generalization properties. In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets. In particular, we find that the functions that GFlowNets learn to approximate have an implicit underlying structure which facilitate generalization. We also find that GFlowNets are sensitive to being trained offline and off-policy; however, the reward implicitly learned by GFlowNets is robust to changes in the training distribution.
2024-12-31
Trans. Mach. Learn. Res. (published)
doi.org
arxiv.org
Investigating the Effect of Providing Required Training to Mothers of Children with Surgery and Its Effect on Mothers' Anxiety
Julia Ferreira
Nadia Safa
Fabio Botelho
Robin Petroze
Hussein Wissanji
Dan Poenaru
Pramod Puligandla
Kenneth Shaw
Maeve Trudeau
Elena Guadagno
Jean Martin Laberge
Sherif Emil
2024-12-31
Journal of Integrative Nursing and Palliative Care (published)
doi.org
Learning-to-Optimize for Consolidation and Transshipment in Multi-store Order Delivery
Xin Wang
Okan Arslan
Jean-François Cordeau
Erick Delage
2024-12-31
SSRN Electronic Journal (accepted)
doi.org
Linking Facial Recognition of Emotions and Socially Shared Regulation in Medical Simulation
Xiaoshan Huang
Tianlong Zhong
Haolun Wu
Yeyu Wang
Ethan Churchill
Xue Liu
David Williamson Shaffer
Computer-supported simulation enables a practical alternative for medical training purposes. This study investigates the co-occurrence of fa… (see more)cial-recognition-derived emotions and socially shared regulation of learning (SSRL) interactions in a medical simulation training context. Using transmodal analysis (TMA), we compare novice and expert learners’ affective and cognitive engagement patterns during collaborative virtual diagnosis tasks. Results reveal that expert learners exhibit strong associations between socio-cognitive interactions and high-arousal emotions (surprise, anger), suggesting focused, effortful engagement. In contrast, novice learners demonstrate stronger links between socio-cognitive processes and happiness or sadness, with less coherent SSRL patterns, potentially indicating distraction or cognitive overload. Transmodal analysis of multimodal data (facial expressions and discourse) highlights distinct regulatory strategies between groups, offering methodological and practical insights for computer-supported cooperative work (CSCW) in medical education. Our findings underscore the role of emotion-regulation dynamics in collaborative expertise development and suggest the need for tailored scaffolding to support novice learners’ socio-cognitive and affective engagement.
2024-12-31
Conference on Computer-Supported Cooperative Work and Social Computing (published)
doi.org
arxiv.org
Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity
Aylin Erman
Julia Ferreira
Waseem Abu Ashour
Elena Guadagno
Etienne St-Louis
Sherif Emil
Jackie Cheung
Dan Poenaru
2024-12-31
Journal of Pediatric Surgery (published)
doi.org
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
Claas Voelcker
Marcel Hussing
Eric R. Eaton
Amir-massoud Farahmand
Igor Gilitschenski
Building deep reinforcement learning (RL) agents that find a good policy with few samples has proven notoriously challenging. To achieve sam… (see more)ple efficiency, recent work has explored updating neural networks with large numbers of gradient steps for every new sample. While such high update-to-data (UTD) ratios have shown strong empirical performance, they also introduce instability to the training process. Previous approaches need to rely on periodic neural network parameter resets to address this instability, but restarting the training process is infeasible in many real-world applications and requires tuning the resetting interval. In this paper, we focus on one of the core difficulties of stable training with limited samples: the inability of learned value functions to generalize to unobserved on-policy actions. We mitigate this issue directly by augmenting the off-policy RL training process with a small amount of data generated from a learned world model. Our method, Model-Augmented Data for Temporal Difference learning (MAD-TD) uses small amounts of generated data to stabilize high UTD training and achieve competitive performance on the most challenging tasks in the DeepMind control suite. Our experiments further highlight the importance of employing a good model to generate data, MAD-TD's ability to combat value overestimation, and its practical stability gains for continued learning.
2024-12-31
ICLR (published)
doi.org
arxiv.org
Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm
Amir Ali Farzin
Yuen-Man Pun
Philipp Braun
Antoine Lesage-Landry
Youssef Diouane
Iman Shames
2024-12-31
Trans. Mach. Learn. Res. (published)
doi.org
arxiv.org
MLOps, LLMOps, FMOps, and Beyond
Chakkrit Kla Tantithamthavorn
Fabio Palomba
Foutse Khomh
Joselito Joey Chua
2024-12-31
IEEE Software (published)
doi.org
A model-free approach for solving choice-based competitive facility location problems using simulation and submodularity
Robin Legault
Emma Frejinger
This paper considers facility location problems in which a firm entering a market seeks to open facilities on a subset of candidate location… (see more)s so as to maximize its expected market share, assuming that customers choose the available alternative that maximizes a random utility function. We introduce a deterministic equivalent reformulation of this stochastic problem as a maximum covering location problem with an exponential number of demand points, each of which is covered by a different set of candidate locations. Estimating the prevalence of these preference profiles through simulation generalizes a sample average approximation method from the literature and results in a maximum covering location problem of manageable size. To solve it, we develop a partial Benders reformulation in which the contribution to the objective of the least influential preference profiles is aggregated and bounded by submodular cuts. This set of profiles is selected by a knee detection method that seeks to identify the best tradeoff between the fraction of the demand that is retained in the master problem and the size of the model. We develop a theoretical analysis of our approach and show that the solution quality it provides for the original stochastic problem, its computational performance, and the automatic profile-retention strategy it exploits are directly connected to the entropy of the preference profiles in the population. Computational experiments indicate that our approach dominates the classical sample average approximation method on large instances, can outperform the best heuristic method from the literature under the multinomial logit model, and achieves state-of-the-art results under the mixed multinomial logit model. We characterize a broader class of problems, which includes assortment optimization, to which the solving methodology and the analyses developed in this paper can be extended.
2024-12-31
INFORMS J. Comput. (published)
doi.org
arxiv.org
Morphometric characteristics of tibial nerve and their relationship with age
Shahram Oveisgharan
Armand Collin
Jingyun Yang
Sue E. Leurgans
Veronique VanderHorst
David A. Bennett
Julien Cohen-Adad
Osvaldo Delbono
Aron S. Buchman
Peripheral nerve comprises a crucial component of the distributed motor/sensory system. However, there is a paucity of data on peripheral ne… (see more)rve morphology derived from large numbers of older adults. This study aimed to quantify the morphometric characteristics of myelinated nerve fibres of the tibial nerve obtained from deceased community-dwelling older adults and examine their association with age. The tibial nerves were obtained from consecutive autopsies of older adults without a history of diabetes who were participants of the Rush Memory and Aging Project, an ongoing longitudinal clinical-autopsy study. A nerve fascicle, obtained from a fixed popliteal segment of the tibial nerve, was separated from the blood vessels and adipose tissue for postmortem examination under an optical microscope. Morphometric characteristics of the myelinated nerve fibres were automatically segmented and quantified using our open-source software AxonDeepSeg. The participants (N = 140) had a mean age of 92.0 years (SD = 5.4) at death, and 72.1% (N = 101) were women. We examined 754 247 myelinated nerve fibres, with an average 5387 (SD = 3436) nerve fibres per participant. The average diameter of myelinated nerve fibres was 4.9 µm (SD = 3.1), axon diameter was 2.0 µm (SD = 1.4), myelin thickness was 1.4 µm (SD = 0.96) and the g-ratio (ratio of axon diameter to myelinated nerve fibre diameter) was 0.45 (SD = 0.17). The relationship between axon diameter and myelin thickness was nonlinear. Myelin was thicker in larger axons up to a diameter of 8 µm, beyond which myelin thickness plateaued. Older age at death was associated with smaller myelinated nerve fibres, smaller axons and thinner myelin. However, age at death was not correlated with myelinated nerve fibre density and was not associated with the average of g-ratio. The association between older age and smaller myelinated nerve fibres was largely attributable to a lower percentage of myelinated nerve fibres >8 µm. We conclude that the smaller tibial myelinated nerve fibres observed in older adults may reflect axonal atrophy rather than degeneration and regeneration of the myelinated nerve fibres. Further research is needed to investigate the pathologies and molecular mechanisms underlying these age-related morphometric changes and their clinical implications in older adults.
2024-12-31
Brain Communications (published)
doi.org