Publications

Interpretability Needs a New Paradigm

Andreas Madsen

Himabindu Lakkaraju

Siva Reddy

A. Chandar

2024-05-07

ArXiv (prépublication)

doi.org

arxiv.org

The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology

Erin E. Gill

Baofeng Jia

Carmen Lia Murall

Raphaël Poujol

Muhammad Zohaib Anwar

Nithu Sara John

Justin Richardsson

Ashley E. Hobb

Abayomi S. Olabode

Alexandru Lepsa

Ana T. Duggan

Andrea D. Tyler

Arnaud N’Guessan

Atul Kachru

Brandon Chan

Catherine Yoshida

Christina K. Yung

David Bujold

Dusan Andric

Edmund Su … (voir 50 de plus)

Emma Griffiths

Gary Van Domselaar

Gordon Jolly

Heather Ward

Henrich Feher

Jared Baker

Jared T. Simpson

Jaser Uddin

Jiannis Ragoussis

Jon Eubank

Fritz, Jörg H.

José Héctor Gálvez

Karen Fang

Kim Cullion

Leonardo Landa Rivera

Linda Xiang

Matthew A. Croxen

Mitchell Shiell

Natalie Prystajecky

Pierre-Olivier Quirion

Rosita Bajari

Samantha Rich

Samira Mubareka

Sandrine Moreira

Scott Cain

Steven G. Sutcliffe

Susanne A. Kraemer

Yann Joly

Yelizar Alturmessov

Cphln Consortium

CanCOGeN Consortium

Academic, VirusSeq Data Portal

network, Health

Marc Fiume

Terrance P Snutch

Cindy Bell

Catalina López-Correa

Julie Hussin

Jeffrey B. Joy

Caroline Colijn

Paul M. K. Gordon

William Hsiao

Art F. Y. Poon

Natalie Knox

Mélanie Courtot

Lincoln Stein

Sarah P. Otto

Guillaume Bourque

B. Jesse Shapiro

Fiona S. L. Brinkman

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform p… (voir plus)ublic health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

2024-05-07

ArXiv (prépublication)

doi.org

arxiv.org

Amortizing Intractable Inference in Large Language Models

Edward J. Hu

Nikolay Malkin

Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This l… (voir plus)imits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve sampling from intractable posterior distributions. We address this limitation by using amortized Bayesian inference to sample from these intractable posteriors. Such amortization is algorithmically achieved by fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets). We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem and demonstrate that our approach enables data-efficient adaptation of LLMs to tasks that require multi-step rationalization and tool use.

2024-05-06

International Conference on Learning Representations (Accept (oral))

doi.org

openreview.net

Balancing Act: Constraining Disparate Impact in Sparse Models

Jose Gallego-Posada

Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or … (voir plus)storage capacities. Although sparse models achieve performance comparable to that of their dense counterparts at the level of the entire dataset, they exhibit high accuracy drops for some data sub-groups. Existing methods to mitigate this disparate impact induced by pruning (i) rely on surrogate metrics that address the problem indirectly and have limited interpretability; or (ii) scale poorly with the number of protected sub-groups in terms of computational cost. We propose a constrained optimization approach that directly addresses the disparate impact of pruning: our formulation bounds the accuracy change between the dense and sparse models, for each sub-group. This choice of constraints provides an interpretable success criterion to determine if a pruned model achieves acceptable disparity levels. Experimental results demonstrate that our technique scales reliably to problems involving large models and hundreds of protected sub-groups.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Bridging State and History Representations: Understanding Self-Predictive RL

Benjamin Eysenbach

Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially obse… (voir plus)rvable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions are, in fact, based on a common idea of self-predictive abstraction. Furthermore, we provide theoretical insights into the widely adopted objectives and optimization, such as the stop-gradient technique, in learning self-predictive representations. These findings together yield a minimalist algorithm to learn self-predictive representations for states and histories. We validate our theories by applying our algorithm to standard MDPs, MDPs with distractors, and POMDPs with sparse rewards. These findings culminate in a set of preliminary guidelines for RL practitioners.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Course Correcting Koopman Representations

Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space. Theor… (voir plus)etically, such features can be used to simplify many problems in modeling and control of NLDS. In this work we study autoencoder formulations of this problem, and different ways they can be used to model dynamics, specifically for future state prediction over long horizons. We discover several limitations of predicting future states in the latent space and propose an inference-time mechanism, which we refer to as Periodic Reencoding, for faithfully capturing long term dynamics. We justify this method both analytically and empirically via experiments in low and high dimensional NLDS.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Cycle Consistency Driven Object Discovery

Aniket Didolkar

Anirudh Goyal

Yoshua Bengio

Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. … (voir plus)Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Delta-AI: Local Objectives for Amortized Inference in Sparse Graphical Models

Jean-Pierre Falet

Hae Beom Lee

Nikolay Malkin

Chen Sun

We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call …

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Diffusion Generative Flow Samplers: Improving Learning Signals Through Partial Trajectory Optimization

Dinghuai Zhang

Ricky T. Q. Chen

Cheng-Hao Liu

Aaron Courville

Yoshua Bengio

We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine lear… (voir plus)ning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes to model approximate samples from these target densities. The main drawback of these approaches is that the training objective requires full trajectories to compute, resulting in sluggish credit assignment issues due to use of entire trajectories and a learning signal present only at the terminal time. In this work, we present Diffusion Generative Flow Samplers (DGFS), a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments, via parameterizing an additional "flow function". Our method takes inspiration from the theory developed for generative flow networks (GFlowNets), allowing us to make use of intermediate learning signals. Through various challenging experiments, we demonstrate that DGFS achieves more accurate estimates of the normalization constant than closely-related prior methods.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

On Diffusion Modeling for Anomaly Detection

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detectio… (voir plus)n. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.

2024-05-06

International Conference on Learning Representations (Accept (spotlight))

doi.org

openreview.net

DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity

Melissa Hall

Candace Ross

Adina Williams

Nicolas Carion

Michal Drozdzal

Adriana Romero Soriano

The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play conte… (voir plus)nt creation solutions make it crucial to understand their potential biases. In this work, we introduce three indicators to evaluate the realism, diversity and prompt-generation consistency of text-to-image generative systems when prompted to generate objects from across the world. Our indicators complement qualitative analysis of the broader impact of such systems by enabling automatic and efficient benchmarking of geographic disparities, an important step towards building responsible visual content creation systems. We use our proposed indicators to analyze potential geographic biases in state-of-the-art visual content creation systems and find that: (1) models have less realism and diversity of generations when prompting for Africa and West Asia than Europe, (2) prompting with geographic information comes at a cost to prompt-consistency and diversity of generated images, and (3) models exhibit more region-level disparities for some objects than others. Perhaps most interestingly, our indicators suggest that progress in image generation quality has come at the cost of real-world geographic representation. Our comprehensive evaluation constitutes a crucial step towards ensuring a positive experience of visual content creation for everyone.

2024-05-06

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Expected Flow Networks in Stochastic Environments and Two-Player Zero-Sum Games

Bilun Sun

Nikolay Malkin

Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully … (voir plus)applied to various structured object generation tasks, sampling a diverse set of high-reward objects quickly. We propose expected flow networks (EFlowNets), which extend GFlowNets to stochastic environments. We show that EFlowNets outperform other GFlowNet formulations in stochastic tasks such as protein design. We then extend the concept of EFlowNets to adversarial environments, proposing adversarial flow networks (AFlowNets) for two-player zero-sum games. We show that AFlowNets learn to find above 80% of optimal moves in Connect-4 via self-play and outperform AlphaZero in tournaments.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

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Boussole des politiques en IA

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La plateforme Mila Ventures

Boussole des politiques en IA

Séances d'information virtuelles

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Publications