Portrait de David Rolnick

David Rolnick

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur adjoint, McGill University, École d'informatique
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Théorie de l'apprentissage automatique

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

Collaborateur·rice alumni - McGill
Collaborateur·rice alumni - UdeM
Collaborateur·rice de recherche - Cambridge University
Co-superviseur⋅e :
Postdoctorat - McGill
Collaborateur·rice de recherche - McGill
Collaborateur·rice de recherche
Collaborateur·rice de recherche - N/A
Co-superviseur⋅e :
Maîtrise recherche - McGill
Collaborateur·rice de recherche - Leipzig University
Collaborateur·rice de recherche
Collaborateur·rice de recherche
Collaborateur·rice de recherche
Visiteur de recherche indépendant
Collaborateur·rice de recherche - UdeM
Collaborateur·rice de recherche - Johannes Kepler University
Collaborateur·rice de recherche - University of Amsterdam
Maîtrise recherche - McGill
Collaborateur·rice de recherche
Visiteur de recherche indépendant - Université de Montréal
Collaborateur·rice de recherche - University of East Anglia
Collaborateur·rice de recherche
Stagiaire de recherche - UdeM
Collaborateur·rice de recherche - Columbia university
Postdoctorat - McGill
Co-superviseur⋅e :
Doctorat - University of Waterloo
Co-superviseur⋅e :
Collaborateur·rice alumni - UdeM
Maîtrise recherche - McGill
Collaborateur·rice de recherche - Columbia university
Collaborateur·rice de recherche - University of Tübingen
Collaborateur·rice de recherche - Karlsruhe Institute of Technology
Doctorat - McGill
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche
Doctorat - McGill
Collaborateur·rice alumni - McGill

Publications

Climate Variable Downscaling with Conditional Normalizing Flows
Christina Winkler
Paula Harder
Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations.… (voir plus) This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.
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
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
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly i… (voir plus)mportant. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.
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.
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 Op… (voir plus)enAI'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 \
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 Op… (voir plus)enAI'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 \
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
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
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.
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.
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
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly i… (voir plus)mportant. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.
Linear Weight Interpolation Leads to Transient Performance Gains