Portrait of Adam M. Oberman

Adam M. Oberman

Associate Academic Member
Canada CIFAR AI Chair
Full Professor, McGill University, Department of Mathematics and Statistics
Research Topics
AI Safety
Deep Learning
Generative Models
Machine Learning Theory
Representation Learning

Biography

I am a professor at McGill University, in the Department of Mathematics and Statistics. My research revolves around the application of advanced mathematical techniques to the field of deep learning. My primary areas of expertise include generative modelling, stochastic optimization methods, fairness/bias removal in computer vision, and generalization in reinforcement learning.

Before joining McGill in 2012, I held a tenured faculty position at Simon Fraser University and completed a postdoctoral fellowship at the University of Texas, Austin. I obtained my undergraduate education at the University of Toronto and pursued graduate studies at the University of Chicago. I have also held visiting positions at the University of California, Los Angeles (UCLA) and at the National Institute for Research in Digital Science and Technology (INRIA) in Paris.

My early research encompassed the fields of partial differential equations and scientific computing, where I made significant contributions to areas like numerical optimal transportation, geometric PDEs and stochastic control problems.

I teach two comprehensive theory courses on machine learning, covering topics such as statistical learning theory and kernel theory.

For prospective graduate students interested in working with me, please apply to both Mila – Quebec Artificial Intelligence Institute and the Department of Mathematics and Statistics at McGill. Alternatively, applicants may consider co-supervision opportunities with advisors from the computer science program at McGill or Université de Montréal.

Current Students

Linh Le
Independent visiting researcher - University of Technology Sydney
Github
Google Scholar
Noah Marshall
PhD - McGill University
Co-supervisor :
Courtney Paquette
Mohamed Amine Merzouk
Postdoctorate - McGill University
Website
Github
Google Scholar
Michael Rizvi-Martel
PhD - Université de Montréal
Principal supervisor :
Guillaume Rabusseau
Website
Github
Google Scholar

Publications

Harnessing small projectors and multiple views for efficient vision pretraining
Arna Ghosh
Kumar Krishna Agrawal
Shagun Sodhani
Adam M. Oberman
Blake A. Richards
Recent progress in self-supervised (SSL) visual representation learning has led to the development of several different proposed frameworks … (see more)that rely on augmentations of images but use different loss functions. However, there are few theoretically grounded principles to guide practice, so practical implementation of each SSL framework requires several heuristics to achieve competitive performance. In this work, we build on recent analytical results to design practical recommendations for competitive and efficient SSL that are grounded in theory. Specifically, recent theory tells us that existing SSL frameworks are minimizing the same idealized loss, which is to learn features that best match the data similarity kernel defined by the augmentations used. We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute. We study the implicit bias of using gradient descent to minimize our reformulated loss function and find that using a stronger orthogonalization constraint with a reduced projector dimensionality should yield good representations. Furthermore, the theory tells us that approximating the reformulated loss should be improved by increasing the number of augmentations, and as such using multiple augmentations should lead to improved convergence. We empirically verify our findings on CIFAR, STL and Imagenet datasets, wherein we demonstrate an improved linear readout performance when training a ResNet-backbone using our theoretically grounded recommendations. Remarkably, we also demonstrate that by leveraging these insights, we can reduce the pretraining dataset size by up to 2
2023-12-31
NeurIPS (published)
doi.org
openreview.net