Publications

A density estimation perspective on learning from pairwise human preferences
Vincent Dumoulin
Daniel D. Johnson
Pablo Samuel Castro
Hugo Larochelle
Yann Dauphin
Learning from human feedback (LHF) -- and in particular learning from pairwise preferences -- has recently become a crucial ingredient in tr… (see more)aining large language models (LLMs), and has been the subject of much research. Most recent works frame it as a reinforcement learning problem, where a reward function is learned from pairwise preference data and the LLM is treated as a policy which is adapted to maximize the rewards, often under additional regularization constraints. We propose an alternative interpretation which centers on the generative process for pairwise preferences and treats LHF as a density estimation problem. We provide theoretical and empirical results showing that for a family of generative processes defined via preference behavior distribution equations, training a reward function on pairwise preferences effectively models an annotator's implicit preference distribution. Finally, we discuss and present findings on"annotator misspecification"-- failure cases where wrong modeling assumptions are made about annotator behavior, resulting in poorly-adapted models -- suggesting that approaches that learn from pairwise human preferences could have trouble learning from a population of annotators with diverse viewpoints.
2024-02-27
TMLR (accepted)
doi.org
openreview.net
A Neural-Evolutionary Algorithm for Autonomous Transit Network Design
Andrew Holliday
Gregory Dudek
2024-02-27
ArXiv (preprint)
doi.org
arxiv.org
RAMEN Unveils Clinical Variable Networks for COVID-19 Severity and Long COVID Using Absorbing Random Walks and Genetic Algorithms
Yiwei Xiong
Jingtao Wang
Xiaoxiao Shang
Tingting Chen
Douglas D. Fraser
Gregory Fonseca
Simon Rousseau
Jun Ding
The COVID-19 pandemic has significantly altered global socioeconomic structures and individual lives. Understanding the disease mechanisms a… (see more)nd facilitating diagnosis requires comprehending the complex interplay among clinical factors like demographics, symptoms, comorbidities, treatments, lab results, complications, and other metrics, and their relation to outcomes such as disease severity and long term outcomes (e.g., post-COVID-19 condition/long COVID). Conventional correlational methods struggle with indirect and directional connections among these factors, while standard graphical methods like Bayesian networks are computationally demanding for extensive clinical variables. In response, we introduced RAMEN, a methodology that integrates Genetic Algorithms with random walks for efficient Bayesian network inference, designed to map the intricate relationships among clinical variables. Applying RAMEN to the Biobanque québécoise de la COVID-19 (BQC19) dataset, we identified critical markers for long COVID and varying disease severity. The Bayesian Network, corroborated by existing literature and supported through multi-omics analyses, highlights significant clinical variables linked to COVID-19 outcomes. RAMEN’s ability to accurately map these connections contributes substantially to developing early and effective diagnostics for severe COVID-19 and long COVID.
2024-02-27
bioRxiv (preprint)
doi.org
On the Societal Impact of Open Foundation Models
Sayash Kapoor
Rishi Bommasani
Kevin Klyman
Shayne Longpre
Ashwin Ramaswami
Peter Cihon
Aspen Hopkins
Kevin Bankston
Stella Biderman
Miranda Bogen
Rumman Chowdhury
Alex Engler
Peter Henderson
Yacine Jernite
Seth Lazar
Stefano Maffulli
Alondra Nelson
Joelle Pineau
Aviya Skowron
Dawn Song … (see 5 more)
Victor Storchan
Daniel Zhang
Daniel E. Ho
Percy Liang
Arvind Narayanan
2024-02-27
ArXiv (preprint)
doi.org
arxiv.org
Effective Latent Differential Equation Models via Attention and Multiple Shooting
Germán Abrevaya
Mahta Ramezanian-Panahi
Jean-Christophe Gagnon-Audet
Pablo Polosecki
Irina Rish
Silvina Ponce Dawson
Guillermo Cecchi
Guillaume Dumas
2024-02-26
TMLR (accepted)
openreview.net
openreview.net
SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning
Luca Zampierin
Ghouthi Boukli Hacene
Bac Nguyen
Mirco Ravanelli
2024-02-26
ArXiv (preprint)
doi.org
arxiv.org
Correction to: Multi-agent reinforcement learning for fast-timescale demand response of residential loads
Vincent Mai
Philippe Maisonneuve
Tianyu Zhang
Hadi Nekoei
Liam Paull
Antoine Lesage-Landry
2024-02-23
Machine-mediated learning (published)
doi.org
Intra-Host Evolution Analyses in an Immunosuppressed Patient Supports SARS-CoV-2 Viral Reservoir Hypothesis
Dominique Fournelle
Fatima Mostefai
Elsa Brunet-Ratnasingham
Raphael Poujol
Jean-Christophe Grenier
José Héctor Gálvez
Amélie Pagliuzza
Inès Levade
Sandrine Moreira
Mehdi Benlarbi
Guillaume Beaudoin-Bussières
Gabrielle Gendron-Lepage
Catherine Bourassa
Alexandra Tauzin
Simon Grandjean Lapierre
Nicolas Chomont
Andrés Finzi
Daniel E. Kaufmann
Morgan Craig
Julie Hussin
2024-02-23
Viruses (published)
doi.org
The Sample Average Approximation Method for Solving Two-Stage Stochastic Programs with Endogenous Uncertainty
Maria Bazotte
Margarida Carvalho
Thibaut Vidal
Real-world decision-making problems involve Type 1 decision-dependent uncertainty, where the probability distribution of the stochastic proc… (see more)ess depends on the model decisions. However, few studies focus on two-stage stochastic programs with this type of endogenous uncertainty, and those that do lack general methodologies. We thus propose herein a general method for solving a class of these programs based on the transformation of random variables, a technique widely employed in probability and statistics. The proposed method is tailored to large-scale problems with discrete or continuous endogenous random variables. The random variable transformation allows the use of the sample average approximation (SAA) method, which provides optimality convergence guarantees under certain conditions. We show that, for some classical distributions, the proposed method reduces to solving mixed-integer linear or convex programs. Finally, we validate this method by applying it to a network design and facility-protection problem, considering distinct decision-dependent distributions for the random variables. Whereas most distributions result in a nonlinear nonconvex deterministic equivalent program, the proposed method solves mixed-integer linear programs in all cases. In addition, it produces attractive performance estimators for the SAA method in a reasonable computational time and outperforms the case in which the endogenous distribution defines a mixed-integer deterministic equivalent.
2024-02-23
ArXiv (preprint)
arxiv.org
arxiv.org
Posterior inference of Hi-C contact frequency through sampling
Yanlin Zhang
Christopher J. F. Cameron
Mathieu Blanchette
Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are r… (see more)epresented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.
2024-02-22
Frontiers in Bioinformatics (published)
doi.org
Reinforcement Learning with Elastic Time Steps
Dong Wang
Giovanni Beltrame
2024-02-22
ArXiv (preprint)
doi.org
arxiv.org
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
Lucas Lehnert
Sainbayar Sukhbaatar
Paul McVay
Michael Rabbat
Yuandong Tian
While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symboli… (see more)c planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard
2024-02-21
ArXiv (preprint)
doi.org
arxiv.org