Adam M. Oberman

Publication Activity (10 Years)

Years Active: 2003-2023
Publications (10 Years): 40

Top Topics

Differential Equations

Numerical Solution

Top Venues

J. Sci. Comput.

J. Comput. Phys.

Publications

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)
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 (2022)
Mariana Oliveira Prazeres, Xinlin Li, Adam M. Oberman, Vahid Partovi Nia
EuclidNets: Combining Hardware and Architecture Design for Efficient Training and Inference. ICPRAM (2022)
Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam M. Oberman
FairCal: Fairness Calibration for Face Verification. ICLR (2022)
Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. CoRR (2022)
Vikram Voleti, Christopher Pal, Adam M. Oberman
Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models. CoRR (2022)
Tiago Salvador, Vikram Voleti, Alexander Iannantuono, Adam M. Oberman
Improved Predictive Uncertainty using Corruption-based Calibration. CoRR (2021)
Vikram Voleti, Chris Finlay, Adam M. Oberman, Christopher J. Pal
Multi-Resolution Continuous Normalizing Flows. CoRR (2021)
Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam M. Oberman
Bias Mitigation of Face Recognition Models Through Calibration. CoRR (2021)
Mariana Prazeres, Adam M. Oberman
Stochastic Gradient Descent with Polyak's Learning Rate. J. Sci. Comput. 89 (1) (2021)
Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman
How to train your neural ODE. CoRR (2020)
Adam M. Oberman, Tiago Salvador
A Partial Differential Equation Obstacle Problem for the Level Set Approach to Visibility. J. Sci. Comput. 82 (1) (2020)
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)
Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman
A principled approach for generating adversarial images under non-smooth dissimilarity metrics. AISTATS (2020)
Ryan Campbell, Chris Finlay, Adam M. Oberman
Adversarial Boot Camp: label free certified robustness in one epoch. CoRR (2020)
Chris Finlay, Augusto Gerolin, Adam M. Oberman, Aram-Alexandre Pooladian
Learning normalizing flows from Entropy-Kantorovich potentials. CoRR (2020)
Maxime Laborde, Adam M. Oberman
A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case. AISTATS (2020)
Levon Nurbekyan, Alexander Iannantuono, Adam M. Oberman
No-Collision Transportation Maps. J. Sci. Comput. 82 (2) (2020)
Ryan Campbell, Chris Finlay, Adam M. Oberman
Deterministic Gaussian Averaged Neural Networks. CoRR (2020)
Chris Finlay, Adam M. Oberman
Improved Accuracy of Monotone Finite Difference Schemes on Point Clouds and Regular Grids. SIAM J. Sci. Comput. 41 (5) (2019)
Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman
The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information. ICCV (2019)
Chris Finlay, Adam M. Oberman
Scaleable input gradient regularization for adversarial robustness. CoRR (2019)
Aram-Alexandre Pooladian, Chris Finlay, Adam M. Oberman
Farkas layers: don't shift the data, fix the geometry. CoRR (2019)
Adam M. Oberman, Tiago Salvador
A Partial Differential Equation Obstacle Problem for the Level Set Approach to Visibility. CoRR (2019)
Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman
The LogBarrier adversarial attack: making effective use of decision boundary information. CoRR (2019)
Chris Finlay, Adam M. Oberman
Empirical confidence estimates for classification by deep neural networks. CoRR (2019)
Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman
A principled approach for generating adversarial images under non-smooth dissimilarity metrics. CoRR (2019)
Adam M. Oberman
Partial differential equation regularization for supervised machine learning. CoRR (2019)
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) (2018)
Bilal Abbasi, Adam M. Oberman
Computing the Level Set Convex Hull. J. Sci. Comput. 75 (1) (2018)
Adam M. Oberman, Jeff Calder
Lipschitz regularized Deep Neural Networks converge and generalize. CoRR (2018)
Bilal Abbasi, Jeff Calder, Adam M. Oberman
Anomaly Detection and Classification for Streaming Data using PDEs. SIAM J. Appl. Math. 78 (2) (2018)
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) (2018)
Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier
Partial differential equations for training deep neural networks. ACSSC (2017)
Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier
Deep Relaxation: partial differential equations for optimizing deep neural networks. CoRR (2017)
Jun Liu, Brittany D. Froese, Adam M. Oberman, Mingqing Xiao
A multigrid scheme for 3D Monge-Ampère equations. Int. J. Comput. Math. 94 (9) (2017)
Penghang Yin, Minh Pham, Adam M. Oberman, Stanley J. Osher
Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-means Clustering. CoRR (2017)
Robert Graham, Adam M. Oberman
Approximate Convex Hulls. CoRR (2017)
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) (2016)
Bilal Abbasi, Jeff Calder, Adam M. Oberman
Anomaly detection and classification for streaming data using partial differential equations. CoRR (2016)
Adam M. Oberman, Tiago Salvador
Filtered schemes for Hamilton-Jacobi equations: A simple construction of convergent accurate difference schemes. J. Comput. Phys. 284 (2015)
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 (2014)
Jun Liu, Brittany D. Froese, Adam M. Oberman, Mingqing Xiao
A multigrid solver for the three dimensional Monge-Ampère equation. CoRR (2014)
Yanghong Huang, Adam M. Oberman
Numerical Methods for the Fractional Laplacian: A Finite Difference-Quadrature Approach. SIAM J. Numer. Anal. 52 (6) (2014)
Brittany D. Froese, Adam M. Oberman
Convergent Filtered Schemes for the Monge-Ampère Partial Differential Equation. SIAM J. Numer. Anal. 51 (1) (2013)
Adam M. Oberman
p-Laplace equations. J. Comput. Appl. Math. 254 (2013)
Adam M. Oberman
A Numerical Method for Variational Problems with Convexity Constraints. SIAM J. Sci. Comput. 35 (1) (2013)
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) (2011)
Brittany D. Froese, Adam M. Oberman
Fast finite difference solvers for singular solutions of the elliptic Monge-Ampère equation. J. Comput. Phys. 230 (3) (2011)
Brittany D. Froese, Adam M. Oberman
Fast finite difference solvers for singular solutions of the elliptic Monge-Ampére equation CoRR (2010)
Adam M. Oberman, Ryo Takei, Alexander Vladimirsky
Homogenization of Metric Hamilton-Jacobi Equations. Multiscale Model. Simul. 8 (1) (2009)
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) (2006)
Diogo A. Gomes, Adam M. Oberman
Computing the Effective Hamiltonian using a Variational Approach. CDC/ECC (2005)
Adam M. Oberman
A convergent difference scheme for the infinity Laplacian: construction of absolutely minimizing Lipschitz extensions. Math. Comput. 74 (251) (2005)
Adam M. Oberman
A convergent monotone difference scheme for motion of level sets by mean curvature. Numerische Mathematik 99 (2) (2004)
Diogo A. Gomes, Adam M. Oberman
Computing the Effective Hamiltonian Using a Variational Approach. SIAM J. Control. Optim. 43 (3) (2004)
Adam M. Oberman, Thaleia Zariphopoulou
Pricing early exercise contracts in incomplete markets. Comput. Manag. Sci. 1 (1) (2003)