David Rolnick

Biographie

David Rolnick est professeur adjoint et titulaire d’une chaire en IA Canada-CIFAR à l'École d'informatique de l'Université McGill et membre académique principal de Mila – Institut québécois d’intelligence artificielle. Ses travaux portent sur les applications de l'apprentissage automatique dans la lutte contre le changement climatique. Il est cofondateur et président de Climate Change AI et codirecteur scientifique de Sustainability in the Digital Age. David Rolnick a obtenu un doctorat en mathématiques appliquées du Massachusetts Institute of Technology (MIT). Il a été chercheur postdoctoral en sciences mathématiques à la National Science Foundation (NSF), chercheur diplômé à la NSF et boursier Fulbright. Il a figuré sur la liste des « 35 innovateurs de moins de 35 ans » de la MIT Technology Review en 2021.

Étudiants actuels

Benjamin Akera Binen

Maîtrise recherche - McGill

Collaborateur·rice alumni - UdeM

Mohit Anand

Collaborateur·rice de recherche - The University of Dresden, Helmholtz Centre for Environmental Research Leipzig

Ayush Ayush Prasad

Collaborateur·rice de recherche

Nikolaos Ioannis Bountos

Collaborateur·rice de recherche - National Observatory of Athens

Collaborateur·rice de recherche

Michael Bunsen

Collaborateur·rice de recherche - McGill

Juan Sebastián Cañas

Collaborateur·rice de recherche

Collaborateur·rice de recherche - KU Leuven

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Co-superviseur⋅e :

Yuyan Chen

Maîtrise recherche - McGill

Eya Cherif

Stagiaire de recherche

Fagner Cunha

Collaborateur·rice alumni

Collaborateur·rice de recherche - Université Paris-Saclay

Co-superviseur⋅e :

Amna El-Mustafa

Collaborateur·rice de recherche

Paula Harder

Collaborateur·rice de recherche

Alex Hernandez-Garcia

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Christina Humer

Stagiaire de recherche - Johannes Kepler University

Gaurav Iyer

Maîtrise recherche - McGill

Doctorat - McGill

Devin Kwok

Doctorat - McGill

Vanessa Mac

Collaborateur·rice de recherche - University of Waterloo

David Alexandre Mickisch

Collaborateur·rice de recherche

Chandni Nagda

Collaborateur·rice de recherche

Felix Andreas Nahrstedt

Stagiaire de recherche - UdeM

Arthur Ouaknine

Postdoctorat - McGill

Co-superviseur⋅e :

Lena Podina

Doctorat - University of Waterloo

Co-superviseur⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche

Marlena Reil

Maîtrise recherche - Osnabrueck university

Theo Saulus

Collaborateur·rice de recherche

Co-superviseur⋅e :

Doctorat - McGill

Collaborateur·rice de recherche

seth.pratinav@gmail.com

Collaborateur·rice de recherche

Co-superviseur⋅e :

Ilija Trajković

Collaborateur·rice de recherche - Karlsruhe Institute of Technology

Doctorat - McGill

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche

anna.viklund@mila.quebec

Viktor Viktor Todosijevic

Collaborateur·rice de recherche - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)

Co-superviseur⋅e :

Doctorat - McGill

Postdoctorat - McGill

Christina Winkler

Collaborateur·rice de recherche

Gintare Karolina Dziugaite

Qidong Yang

Collaborateur·rice de recherche

qidong.yang@mila.quebec

Publications

Linear Weight Interpolation Leads to Transient Performance Gains

Gaurav Iyer

2024-09-28

TMLR (accepté)

Pushing the frontiers in climate modelling and analysis with machine learning

Veronika Eyring

William D. Collins

Pierre Gentine

Elizabeth A. Barnes

Marcelo Barreiro

Tom Beucler

Marc Bocquet

Christopher S. Bretherton

Hannah M. Christensen

Katherine Dagon

David John Gagne

David Hall

Dorit Hammerling

Stephan Hoyer

Fernando Iglesias-Suarez

Ignacio Lopez-Gomez

Marie C. McGraw

Gerald A. Meehl

Maria J. Molina

Claire Monteleoni … (voir 9 de plus)

Juliane Mueller

Michael S. Pritchard

Jakob Runge

Philip Stier

Oliver Watt-Meyer

Katja Weigel

Rose Yu

Laure Zanna

2024-08-23

Nature Climate Change (publié)

Tree semantic segmentation from aerial image time series

Venkatesh Ramesh

Arthur Ouaknine

2024-07-18

ArXiv (prépublication)

Stealing part of a production language model

Nicholas Carlini

Daniel Paleka

Krishnamurthy Dj Dvijotham

Thomas Steinke

Jonathan Hayase

A. Feder Cooper

Katherine Lee

Matthew Jagielski

Milad Nasr

Arthur Conmy

Eric Wallace

Florian Tramèr

We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like … (voir plus)OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \\

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

Improving Molecular Modeling with Geometric GNNs: an Empirical Study

Ali Ramlaoui

Théo Saulus

Basile Terver

Victor Schmidt

Fragkiskos D. Malliaros

Alexandre AGM Duval

2024-06-17

ICML.cc/2024/Workshop/ML4LMS (poster)

Gintare Karolina Dziugaite

Linear Weight Interpolation Leads to Transient Performance Gains

Gaurav Iyer

2024-06-16

ICML.cc/2024/Workshop/HiLD (poster)

The Butterfly Effect: Tiny Perturbations Cause Neural Network Training to Diverge

Gül Sena Altıntaş

Devin Kwok

Neural network training begins with a chaotic phase in which the network is sensitive to small perturbations, such as those caused by stocha… (voir plus)stic gradient descent (SGD). This sensitivity can cause identically initialized networks to diverge both in parameter space and functional similarity. However, the exact degree to which networks are sensitive to perturbation, and the sensitivity of networks as they transition out of the chaotic phase, is unclear. To address this uncertainty, we apply a controlled perturbation at a single point in training time and measure its effect on otherwise identical training trajectories. We find that both the

2024-06-16

ICML.cc/2024/Workshop/HiLD (poster)

Position: Application-Driven Innovation in Machine Learning

Alan Aspuru-Guzik

Sara Beery

Bistra Dilkina

Priya L. Donti

Marzyeh Ghassemi

Hannah Kerner

Claire Monteleoni

Esther Rolf

Milind Tambe

Adam White

2024-05-01

ICML.cc/2024/Conference (poster)

proceedings.mlr.press

Predicting Species Occurrence Patterns from Partial Observations

Hager Radi

Mélisande Teng

To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are chang… (voir plus)ing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.

2024-03-26

ArXiv (prépublication)

Stealing Part of a Production Language Model

Nicholas Carlini

Daniel Paleka

Krishnamurthy Dvijotham

Thomas Steinke

Jonathan Hayase

A. Feder Cooper

Katherine Lee

Matthew Jagielski

Milad Nasr

Arthur Conmy

Eric Wallace

Florian Tramèr

2024-03-11

ArXiv (prépublication)

Tackling Climate Change with Machine Learning: Fostering the Maturity of ML Applications for Climate Change

Shiva Madadkhani

Olivia Mendivil Ramos

Millie Chapman

Jesse Dunietz

Arthur Ouaknine

2024-03-08

ICLR.cc/2024/Workshop_Proposals (publié)

Gintare Karolina Dziugaite

Dataset Difficulty and the Role of Inductive Bias

Devin Kwok

Nikhil Anand

Jonathan Frankle

Motivated by the goals of dataset pruning and defect identification, a growing body of methods have been developed to score individual examp… (voir plus)les within a dataset. These methods, which we call"example difficulty scores", are typically used to rank or categorize examples, but the consistency of rankings between different training runs, scoring methods, and model architectures is generally unknown. To determine how example rankings vary due to these random and controlled effects, we systematically compare different formulations of scores over a range of runs and model architectures. We find that scores largely share the following traits: they are noisy over individual runs of a model, strongly correlated with a single notion of difficulty, and reveal examples that range from being highly sensitive to insensitive to the inductive biases of certain model architectures. Drawing from statistical genetics, we develop a simple method for fingerprinting model architectures using a few sensitive examples. These findings guide practitioners in maximizing the consistency of their scores (e.g. by choosing appropriate scoring methods, number of runs, and subsets of examples), and establishes comprehensive baselines for evaluating scores in the future.

2024-01-03

ArXiv (prépublication)