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ORCIDAdam M. Oberman
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- affiliation: McGill University
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Journal Articles
- 2024
[j27]Vikram Voleti, Chris Finlay, Adam Oberman, Christopher Pal:
Multi-resolution continuous normalizing flows. Ann. Math. Artif. Intell. 92(5): 1295-1317 (2024)- 2023
- [j26]Xinlin Li, Mariana Parazeres, Adam Oberman, Alireza Ghaffari, Masoud Asgharian, Vahid Partovi Nia
:
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models. SN Comput. Sci. 4(5): 507 (2023) - [j25]Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman:
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. Trans. Mach. Learn. Res. 2023 (2023) - 2021
- [j24]Mariana Oliveira Prazeres, Adam M. Oberman:
Stochastic Gradient Descent with Polyak's Learning Rate. J. Sci. Comput. 89(1): 25 (2021) - 2020
- [j23]Adam M. Oberman, Tiago Salvador:
A Partial Differential Equation Obstacle Problem for the Level Set Approach to Visibility. J. Sci. Comput. 82(1): 14 (2020) - [j22]Levon Nurbekyan, Alexander Iannantuono, Adam M. Oberman:
No-Collision Transportation Maps. J. Sci. Comput. 82(2): 45 (2020) - 2019
- [j21]Chris Finlay, Adam M. Oberman:
Improved Accuracy of Monotone Finite Difference Schemes on Point Clouds and Regular Grids. SIAM J. Sci. Comput. 41(5): A3097-A3117 (2019) - 2018
- [j20]Bilal Abbasi, Adam M. Oberman:
Computing the Level Set Convex Hull. J. Sci. Comput. 75(1): 26-42 (2018) - [j19]Chris Finlay, Adam M. Oberman:
Approximate Homogenization of Fully Nonlinear Elliptic PDEs: Estimates and Numerical Results for Pucci Type Equations. J. Sci. Comput. 77(2): 936-949 (2018) - [j18]Penghang Yin, Minh Pham, Adam M. Oberman, Stanley J. Osher:
Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-Means Clustering. J. Sci. Comput. 77(2): 1133-1146 (2018) - [j17]Bilal Abbasi, Jeff Calder, Adam M. Oberman:
Anomaly Detection and Classification for Streaming Data using PDEs. SIAM J. Appl. Math. 78(2): 921-941 (2018) - 2017
- [j16]Jun Liu, Brittany D. Froese, Adam M. Oberman, Mingqing Xiao:
A multigrid scheme for 3D Monge-Ampère equations. Int. J. Comput. Math. 94(9): 1850-1866 (2017) - 2016
- [j15]Adam M. Oberman, Ian Zwiers:
Adaptive Finite Difference Methods for Nonlinear Elliptic and Parabolic Partial Differential Equations with Free Boundaries. J. Sci. Comput. 68(1): 231-251 (2016) - 2015
- [j14]Adam M. Oberman, Tiago Salvador:
Filtered schemes for Hamilton-Jacobi equations: A simple construction of convergent accurate difference schemes. J. Comput. Phys. 284: 367-388 (2015) - 2014
- [j13]Jean-David Benamou, Brittany D. Froese, Adam M. Oberman:
Numerical solution of the Optimal Transportation problem using the Monge-Ampère equation. J. Comput. Phys. 260: 107-126 (2014) - [j12]Yanghong Huang, Adam M. Oberman:
Numerical Methods for the Fractional Laplacian: A Finite Difference-Quadrature Approach. SIAM J. Numer. Anal. 52(6): 3056-3084 (2014) - 2013
- [j11]Adam M. Oberman:
Finite difference methods for the Infinity Laplace and pp-Laplace equations. J. Comput. Appl. Math. 254: 65-80 (2013) - [j10]Brittany D. Froese, Adam M. Oberman:
Convergent Filtered Schemes for the Monge-Ampère Partial Differential Equation. SIAM J. Numer. Anal. 51(1): 423-444 (2013) - [j9]Adam M. Oberman:
A Numerical Method for Variational Problems with Convexity Constraints. SIAM J. Sci. Comput. 35(1) (2013) - 2011
- [j8]Brittany D. Froese, Adam M. Oberman:
Fast finite difference solvers for singular solutions of the elliptic Monge-Ampère equation. J. Comput. Phys. 230(3): 818-834 (2011) - [j7]Brittany D. Froese, Adam M. Oberman:
Convergent Finite Difference Solvers for Viscosity Solutions of the Elliptic Monge-Ampère Equation in Dimensions Two and Higher. SIAM J. Numer. Anal. 49(4): 1692-1714 (2011) - 2009
- [j6]Adam M. Oberman, Ryo Takei, Alexander Vladimirsky:
Homogenization of Metric Hamilton-Jacobi Equations. Multiscale Model. Simul. 8(1): 269-295 (2009) - 2006
- [j5]Adam M. Oberman:
Convergent Difference Schemes for Degenerate Elliptic and Parabolic Equations: Hamilton-Jacobi Equations and Free Boundary Problems. SIAM J. Numer. Anal. 44(2): 879-895 (2006) - 2005
- [j4]Adam M. Oberman:
A convergent difference scheme for the infinity Laplacian: construction of absolutely minimizing Lipschitz extensions. Math. Comput. 74(251): 1217-1230 (2005) - 2004
- [j3]Adam M. Oberman:
A convergent monotone difference scheme for motion of level sets by mean curvature. Numerische Mathematik 99(2): 365-379 (2004) - [j2]Diogo A. Gomes, Adam M. Oberman:
Computing the Effective Hamiltonian Using a Variational Approach. SIAM J. Control. Optim. 43(3): 792-812 (2004) - 2003
- [j1]Adam M. Oberman, Thaleia Zariphopoulou:
Pricing early exercise contracts in incomplete markets. Comput. Manag. Sci. 1(1): 75-107 (2003)
Conference and Workshop Papers
- 2024
- [c10]Arna Ghosh, Kumar Krishna Agrawal, Shagun Sodhani, Adam Oberman, Blake A. Richards:
Harnessing small projectors and multiple views for efficient vision pretraining. NeurIPS 2024 - 2022
- [c9]Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. AISTATS 2022: 4132-4157 - [c8]Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam M. Oberman:
FairCal: Fairness Calibration for Face Verification. ICLR 2022 - [c7]Mariana Oliveira Prazeres, Xinlin Li, Adam M. Oberman, Vahid Partovi Nia:
EuclidNets: Combining Hardware and Architecture Design for Efficient Training and Inference. ICPRAM 2022: 141-151 - 2020
- [c6]Maxime Laborde, Adam M. Oberman:
A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case. AISTATS 2020: 602-612 - [c5]Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman:
A principled approach for generating adversarial images under non-smooth dissimilarity metrics. AISTATS 2020: 1442-1452 - [c4]Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman:
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization. ICML 2020: 3154-3164 - 2019
- [c3]Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman:
The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information. ICCV 2019: 4861-4869 - 2017
- [c2]Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier:
Partial differential equations for training deep neural networks. ACSSC 2017: 1627-1631 - 2005
- [c1]Diogo A. Gomes, Adam M. Oberman:
Computing the Effective Hamiltonian using a Variational Approach. CDC/ECC 2005: 729-733
Parts in Books or Collections
- 2020
- [p1]Adam M. Oberman:
Partial differential equation regularization for supervised machine learning. 75 Years of Mathematics of Computation 2020
Informal and Other Publications
- 2023
- [i26]Kumar Krishna Agrawal, Arna Ghosh, Adam Oberman, Blake A. Richards:
Addressing Sample Inefficiency in Multi-View Representation Learning. CoRR abs/2312.10725 (2023) - 2022
- [i25]Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. CoRR abs/2203.00543 (2022) - [i24]Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman:
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. CoRR abs/2210.01210 (2022) - [i23]Vikram Voleti, Christopher Pal, Adam M. Oberman:
Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models. CoRR abs/2210.12254 (2022) - [i22]Xinlin Li, Mariana Parazeres, Adam M. Oberman, Alireza Ghaffari, Masoud Asgharian, Vahid Partovi Nia:
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models. CoRR abs/2212.11803 (2022) - 2021
- [i21]Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam M. Oberman:
Bias Mitigation of Face Recognition Models Through Calibration. CoRR abs/2106.03761 (2021) - [i20]Tiago Salvador, Vikram Voleti, Alexander Iannantuono, Adam M. Oberman:
Improved Predictive Uncertainty using Corruption-based Calibration. CoRR abs/2106.03762 (2021) - [i19]Vikram Voleti, Chris Finlay, Adam M. Oberman, Christopher J. Pal:
Multi-Resolution Continuous Normalizing Flows. CoRR abs/2106.08462 (2021) - 2020
- [i18]Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman:
How to train your neural ODE. CoRR abs/2002.02798 (2020) - [i17]Chris Finlay, Augusto Gerolin, Adam M. Oberman, Aram-Alexandre Pooladian:
Learning normalizing flows from Entropy-Kantorovich potentials. CoRR abs/2006.06033 (2020) - [i16]Ryan Campbell, Chris Finlay, Adam M. Oberman:
Deterministic Gaussian Averaged Neural Networks. CoRR abs/2006.06061 (2020) - [i15]Ryan Campbell, Chris Finlay, Adam M. Oberman:
Adversarial Boot Camp: label free certified robustness in one epoch. CoRR abs/2010.02508 (2020) - 2019
- [i14]Chris Finlay, Adam M. Oberman:
Empirical confidence estimates for classification by deep neural networks. CoRR abs/1903.09215 (2019) - [i13]Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman:
The LogBarrier adversarial attack: making effective use of decision boundary information. CoRR abs/1903.10396 (2019) - [i12]Chris Finlay, Adam M. Oberman:
Scaleable input gradient regularization for adversarial robustness. CoRR abs/1905.11468 (2019) - [i11]Adam M. Oberman, Tiago Salvador:
A Partial Differential Equation Obstacle Problem for the Level Set Approach to Visibility. CoRR abs/1908.00578 (2019) - [i10]Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman:
A principled approach for generating adversarial images under non-smooth dissimilarity metrics. CoRR abs/1908.01667 (2019) - [i9]Adam M. Oberman:
Partial differential equation regularization for supervised machine learning. CoRR abs/1910.01612 (2019) - [i8]Aram-Alexandre Pooladian, Chris Finlay, Adam M. Oberman:
Farkas layers: don't shift the data, fix the geometry. CoRR abs/1910.02840 (2019) - 2018
- [i7]Adam M. Oberman, Jeff Calder:
Lipschitz regularized Deep Neural Networks converge and generalize. CoRR abs/1808.09540 (2018) - 2017
- [i6]Robert Graham, Adam M. Oberman:
Approximate Convex Hulls. CoRR abs/1703.01350 (2017) - [i5]Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier:
Deep Relaxation: partial differential equations for optimizing deep neural networks. CoRR abs/1704.04932 (2017) - [i4]Penghang Yin, Minh Pham, Adam M. Oberman, Stanley J. Osher:
Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-means Clustering. CoRR abs/1710.07746 (2017) - 2016
- [i3]Bilal Abbasi, Jeff Calder, Adam M. Oberman:
Anomaly detection and classification for streaming data using partial differential equations. CoRR abs/1608.04348 (2016) - 2014
- [i2]Jun Liu, Brittany D. Froese, Adam M. Oberman, Mingqing Xiao:
A multigrid solver for the three dimensional Monge-Ampère equation. CoRR abs/1411.7018 (2014) - 2010
- [i1]Brittany D. Froese, Adam M. Oberman:
Fast finite difference solvers for singular solutions of the elliptic Monge-Ampére equation. CoRR abs/1006.5748 (2010)
Coauthor Index
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