Publications

TEARS: Text Representations for Scrutable Recommendations.
Emiliano Penaloza
Olivier Gouvert
Haolun Wu
Laurent Charlin
Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque … (see more)representations that lack interpretability. Moreover, these systems offer limited control to users over their recommendations. Inspired by recent work, we introduce TExtuAl Representations for Scrutable recommendations (TEARS) to address these challenges. Instead of representing a user's interests through a latent embedding, TEARS encodes them in natural text, providing transparency and allowing users to edit them. To do so, TEARS uses a modern LLM to generate user summaries based on user preferences. Using these summaries, we take a hybrid approach where we use an optimal transport procedure to align the summaries' representation with the learned representation of a standard VAE for collaborative filtering. We find this approach can surpass the performance of popular VAE models while providing user-controllable recommendations. We also analyze the controllability of TEARS through three simulated user tasks to evaluate the effectiveness of a user editing its summary. A more detailed version of this manuscript with more experiments, baselines and detail is provided on arXiv.
2025-01-28
ACM.org/TheWebConf/2025/Conference (poster)
doi.org
openreview.net
Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution
Zehao Jin
Mario Pasquato
Benjamin L. Davis
Tristan Deleu
Yu Luo
Changhyun Cho
Pablo Lemos
Laurence Perreault-Levasseur
Yoshua Bengio
Xi Kang
Andrea Valerio Maccio
Yashar Hezaveh
Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure… (see more) (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)--galaxy interaction has long been constrained by observed scaling relations, that is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date dataset, reveals a causal link between galaxy properties and dynamically-measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.
2025-01-27
The Astrophysical Journal (published)
doi.org
arxiv.org
Associations between circulating amino acids and metabolic dysfunction‐associated steatotic liver disease in individuals living with severe obesity
Ina Maltais‐Payette
Jérôme Bourgault
Marie‐Frédérique Gauthier
Laurent Biertho
Simon Marceau
François Julien
Patricia L. Mitchell
Christian Couture
Francis Brière
J. Corbeil
Benoit J. Arsenault
André Tchernof
2025-01-26
Physiological Reports (published)
doi.org
scGraphETM: Graph-Based Deep Learning Approach for Unraveling Cell Type-Specific Gene Regulatory Networks from Single-Cell Multi-Omics Data
Wenqi Dong
Manqi Zhou
Boyu Han
Yi Wang
Yuemei Li
2025-01-26
bioRxiv (preprint)
doi.org
A multivariable prediction model for invasive pulmonary aspergillosis in immunocompromised patients with acute respiratory failure (IPA-GRRR-OH score).
Alice Friol
Guillaume Dumas
Frédéric Pène
Alexandre Demoule
Achille Kouatchet
Laurent Argaud
Naike Bigé
Anne-Sophie Moreau
François Barbier
Djamel Mokart
Virginie Lemiale
Elie Azoulay
2025-01-23
Intensive Care Medicine (published)
doi.org
Meeting and Missing Minds: Children and Adults Use Alignment of Intuitions to Solve Pure Coordination Games
Daniel Perez-Zapata
Xavia McKenzie-Smart
Ian Charest
Ian Apperly
In pure coordination games players seek to coordinate responses with one another without communicating. Without a logically correct response… (see more), success depends upon players intuiting a response that is mutually obvious. Previous work suggests that such coordination requires a distinctive form of “group” thinking and sufficient mutual knowledge, but reveals little about the basis for the intuitive judgements themselves. Here, that question was addressed for the first time by examining the basis of coordination performance of groups whose intuitions might plausibly differ: children versus adults. Twenty-five 5-year-olds, 30 7-year-olds, and 25 adults undertook four types of coordination game, and novel metrics allowed “intuitive alignment” in responses to be evaluated within- and between-groups. All groups performed above chance, and adults showed higher levels of alignment than children, but adults and children showed different patterns in their intuitions. Implications for intergenerational understanding and mis-understanding are discussed.
2025-01-22
Journal of Cognitive Psychology (published)
doi.org
Pitfalls of Evidence-Based AI Policy
Stephen Casper
David M. Krueger
Dylan Hadfield-Menell
Nations across the world are working to govern AI. However, from a technical perspective, the best way to do this is not yet clear. Meanwhil… (see more)e, recent debates over AI regulation have led to calls for “evidence-based AI policy” which emphasize holding regulatory action to a high evidentiary standard. Evidence is of irreplaceable value to policymaking. However, holding regulatory action to too high an evidentiary standard can lead to systematic neglect of certain risks. In historical policy debates (e.g., over tobacco ca. 1965 and fossil fuels ca. 1990) “evidence-based policy” rhetoric is also a well-precedented strategy to downplay the urgency of action, delay regulation, and protect industry interests. Here, we argue that if the goal is evidence-based AI policy, the first regulatory objective must be to actively facilitate the process of identifying, studying, and deliberating about AI risks. We discuss a set of 16 regulatory goals to facilitate this and show that the EU, UK, USA, Brazil, Canada, and China all have substantial opportunities to adopt further evidence-seeking policies.
2025-01-22
ICLR.cc/2025/BlogPosts (accepted)
openreview.net
openreview.net
Robust prior-biased acquisition function for human-in-the-loop Bayesian optimization.
Rose Guay-Hottin
Lison Kardassevitch
Hugo Pham
Guillaume Lajoie
Marco Bonizzato
2025-01-22
Knowledge-Based Systems (published)
doi.org
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection
Yun Zhu
Jia-Chen Gu
Caitlin Sikora
Ho Ko
Yinxiao Liu
Chu-Cheng Lin
Lei Shu
Liangchen Luo
Lei Meng
Bang Liu
Jindong Chen
Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external context… (see more)s. However, the input length grows linearly in the number of retrieved documents, causing a dramatic increase in latency. In this paper, we propose a novel paradigm named Sparse RAG, which seeks to cut computation costs through sparsity. Specifically, Sparse RAG encodes retrieved documents in parallel, which eliminates latency introduced by long-range attention of retrieved documents. Then, LLMs selectively decode the output by only attending to highly relevant caches auto-regressively, which are chosen via prompting LLMs with special control tokens. It is notable that Sparse RAG combines the assessment of each individual document and the generation of the response into a single process. The designed sparse mechanism in a RAG system can facilitate the reduction of the number of documents loaded during decoding for accelerating the inference of the RAG system. Additionally, filtering out undesirable contexts enhances the model’s focus on relevant context, inherently improving its generation quality. Evaluation results on four datasets show that Sparse RAG can be used to strike an optimal balance between generation quality and computational efficiency, demonstrating its generalizability across tasks.
2025-01-21
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
Accelerating neural network training: An analysis of the AlgoPerf competition
Priya Kasimbeg
Frank Schneider
Runa Eschenhagen
Juhan Bae
Chandramouli Shama Sastry
Mark Saroufim
BOYUAN FENG
Less Wright
Edward Z. Yang
Zachary Nado
Sourabh Medapati
Philipp Hennig
Michael G. Rabbat
George E. Dahl
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by i… (see more)mproving the underlying training algorithms. In the external tuning ruleset, submissions must provide workload-agnostic hyperparameter search spaces, while in the self-tuning ruleset they must be completely hyperparameter-free. In both rulesets, submissions are compared on time-to-result across multiple deep learning workloads, training on fixed hardware. This paper presents the inaugural AlgoPerf competition's results, which drew 18 diverse submissions from 10 teams. Our investigation reveals several key findings: (1) The winning submission in the external tuning ruleset, using Distributed Shampoo, demonstrates the effectiveness of non-diagonal preconditioning over popular methods like Adam, even when compared on wall-clock runtime. (2) The winning submission in the self-tuning ruleset, based on the Schedule Free AdamW algorithm, demonstrates a new level of effectiveness for completely hyperparameter-free training algorithms. (3) The top-scoring submissions were surprisingly robust to workload changes. We also discuss the engineering challenges encountered in ensuring a fair comparison between different training algorithms. These results highlight both the significant progress so far, and the considerable room for further improvements.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Action Abstractions for Amortized Sampling
Oussama Boussif
Lena Nehale Ezzine
Joseph Viviano
Michał Koziarski
Moksh Jain
Nikolay Malkin
Emmanuel Bengio
Rim Assouel
Yoshua Bengio
As trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignm… (see more)ent and exploration become more challenging, and the long planning horizon hinders mode discovery and generalization. The challenge is particularly pronounced in entropy-seeking RL methods, such as generative flow networks, where the agent must learn to sample from a structured distribution and discover multiple high-reward states, each of which take many steps to reach. To tackle this challenge, we propose an approach to incorporate the discovery of action abstractions, or high-level actions, into the policy optimization process. Our approach involves iteratively extracting action subsequences commonly used across many high-reward trajectories and `chunking' them into a single action that is added to the action space. In empirical evaluation on synthetic and real-world environments, our approach demonstrates improved sample efficiency performance in discovering diverse high-reward objects, especially on harder exploration problems. We also observe that the abstracted high-order actions are interpretable, capturing the latent structure of the reward landscape of the action space. This work provides a cognitively motivated approach to action abstraction in RL and is the first demonstration of hierarchical planning in amortized sequential sampling.
2025-01-21
International Conference on Learning Representations (poster)
doi.org
openreview.net
Advantage Alignment Algorithms
Juan Duque
Milad Aghajohari
Tim Cooijmans
Razvan Ciuca
Tianyu Zhang
Gauthier Gidel
Aaron Courville
The growing presence of artificially intelligent agents in everyday decision-making, from LLM assistants to autonomous vehicles, hints at a … (see more)future in which conflicts may arise from each agent optimizing individual interests. In general-sum games these conflicts are apparent, where naive Reinforcement Learning agents get stuck in Pareto-suboptimal Nash equilibria. Consequently, opponent shaping has been introduced as a method with success at finding socially beneficial equilibria in social dilemmas. In this work, we introduce Advantage Alignment, a family of algorithms derived from first principles that perform opponent shaping efficiently and intuitively. This is achieved by aligning the advantages of conflicting agents in a given game by increasing the probability of mutually-benefiting actions. We prove that existing opponent shaping methods, including LOLA and LOQA, implicitly perform Advantage Alignment. Compared to these works, Advantage Alignment mathematically simplifies the formulation of opponent shaping and seamlessly works for continuous action domains. We also demonstrate the effectiveness of our algorithm in a wide range of social dilemmas, achieving state of the art results in each case, including a social dilemma version of the Negotiation Game.
2025-01-21
International Conference on Learning Representations (oral)
doi.org
openreview.net