Adam Oberman

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Membre Académique Associé
Adam Oberman
Professeur, McGill University
Adam Oberman

Adam Oberman is a Professor in the Department of Mathematics and Statistics at McGill University, and director of the Applied Mathematics Laboratory at the Centre de Recherches Mathematiques. He has held visiting positions at UCLA. Before coming to McGill in 2012, he was tenured at Simon Fraser university, and a postdoc at University of Texas, Austin. He was a student at the University of Toronto (undergraduate) and University of Chicago (graduate).

His research focusses on mathematical approaches to machine learning: optimization (including stochastic gradient descent), regularization approaches (including averaged models and gradient regularization), and robust models, including adversarially trained models. He teaches a theory course on machine learning, including generalization theory, and a scientific computing course with a focus on high dimensional methods.

Publications

2021-10

Frustratingly Easy Uncertainty Estimation for Distribution Shift.
Tiago Salvador, Vikram Voleti, Alexander Iannantuono and Adam Oberman
arXiv: Machine Learning
(2021-10-18)
arxiv.orgPDF
Stochastic gradient descent with Polyak’s learning rate
Mariana Prazeres and Adam M. Oberman
Journal of Scientific Computing
(2021-10-01)
link.springer.com

2021-06

Multi-Resolution Continuous Normalizing Flows.
Vikram Voleti, Chris Finlay, Adam M. Oberman and Christopher J. Pal
arXiv: Computer Vision and Pattern Recognition
(2021-06-15)
dblp.uni-trier.dePDF
FairCal: Fairness Calibration for Face Verification.
Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall and Adam Oberman
arXiv: Computer Vision and Pattern Recognition
(2021-06-07)
arxiv.orgPDF
Improved Predictive Uncertainty using Corruption-based Calibration.
Tiago Salvador, Vikram Voleti, Alexander Iannantuono and Adam M. Oberman
arXiv preprint arXiv:2106.03762
(2021-06-07)
ui.adsabs.harvard.eduPDF
Bias Mitigation of Face Recognition Models Through Calibration.
Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall and Adam M. Oberman
arXiv preprint arXiv:2106.03761
(2021-06-07)
ui.adsabs.harvard.eduPDF
Improving Continuous Normalizing Flows using a Multi-Resolution Framework
Vikram Voleti, Chris Finlay, Adam M Oberman and Christopher Pal
ICML 2021
(2021-06-02)
openreview.netPDF

2021-05

Uncertainty for deep image classifiers on out of distribution data.
Tiago Salvador, Alexander Iannantuono and Adam M Oberman
(venue unknown)
(2021-05-04)
openreview.netPDF
Adversarial Boot Camp: label free certified robustness in one epoch
Ryan Campbell, Chris Finlay and Adam M Oberman
arXiv e-prints
(2021-05-04)
ui.adsabs.harvard.eduPDF

2021-03

Scaleable input gradient regularization for adversarial robustness
Chris Finlay and Adam M. Oberman
Machine Learning with Applications
(2021-03-15)
www.sciencedirect.comPDF

2020-11

How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan and Adam M. Oberman
ICML 2020
(2020-11-21)
proceedings.mlr.pressPDF

2020-07

How to train your Neural ODE
Chris Finlay, Joern-Henrik Jacobsen, Levon Nurbekyan and Adam M Oberman
ICML 2020
(2020-07-12)
icml.ccPDF

2020-06

Learning normalizing flows from Entropy-Kantorovich potentials
Chris Finlay, Augusto Gerolin, Adam M Oberman and Aram-Alexandre Pooladian
arXiv preprint arXiv:2006.06033
(2020-06-10)
ui.adsabs.harvard.eduPDF
Deterministic Gaussian Averaged Neural Networks.
Ryan Campbell, Chris Finlay and Adam M. Oberman
arXiv: Learning
(2020-06-10)
dblp.uni-trier.dePDF
A principled approach for generating adversarial images under non-smooth dissimilarity metrics
Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel and Adam M. Oberman
AISTATS 2020
(2020-06-03)
proceedings.mlr.pressPDF

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