Adam M. Oberman

58 papers A* 3A 2C 1Misc 1Journal 49Unranked 1

Year	Rank	Type	Title / Venue / Authors
2023	J	`jnl`	A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. Trans. Mach. Learn. Res. Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman
2022	J	`jnl`	A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. CoRR Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman
2022	J	`jnl`	EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models. CoRR Xinlin Li, Mariana Parazeres, Adam M. Oberman, Alireza Ghaffari, Masoud Asgharian, Vahid Partovi Nia
2022	C	`conf`	EuclidNets: Combining Hardware and Architecture Design for Efficient Training and Inference. ICPRAM Mariana Oliveira Prazeres, Xinlin Li, Adam M. Oberman, Vahid Partovi Nia
2022	A*	`conf`	FairCal: Fairness Calibration for Face Verification. ICLR Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam M. Oberman
2022	J	`jnl`	Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models. CoRR Vikram Voleti, Christopher Pal, Adam M. Oberman
2021	J	`jnl`	Bias Mitigation of Face Recognition Models Through Calibration. CoRR Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam M. Oberman
2021	J	`jnl`	Improved Predictive Uncertainty using Corruption-based Calibration. CoRR Tiago Salvador, Vikram Voleti, Alexander Iannantuono, Adam M. Oberman
2021	J	`jnl`	Multi-Resolution Continuous Normalizing Flows. CoRR Vikram Voleti, Chris Finlay, Adam M. Oberman, Christopher J. Pal
2021	J	`jnl`	Stochastic Gradient Descent with Polyak's Learning Rate. J. Sci. Comput. Mariana Oliveira Prazeres, Adam M. Oberman
2020	A	`conf`	A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case. AISTATS Maxime Laborde, Adam M. Oberman
2020	J	`jnl`	A Partial Differential Equation Obstacle Problem for the Level Set Approach to Visibility. J. Sci. Comput. Adam M. Oberman, Tiago Salvador
2020	A	`conf`	A principled approach for generating adversarial images under non-smooth dissimilarity metrics. AISTATS Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman
2020	J	`jnl`	Adversarial Boot Camp: label free certified robustness in one epoch. CoRR Ryan Campbell, Chris Finlay, Adam M. Oberman
2020	J	`jnl`	Deterministic Gaussian Averaged Neural Networks. CoRR Ryan Campbell, Chris Finlay, Adam M. Oberman
2020	A*	`conf`	How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization. ICML Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman
2020	J	`jnl`	How to train your neural ODE. CoRR Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman
2020	J	`jnl`	Learning normalizing flows from Entropy-Kantorovich potentials. CoRR Chris Finlay, Augusto Gerolin, Adam M. Oberman, Aram-Alexandre Pooladian
2020	J	`jnl`	No-Collision Transportation Maps. J. Sci. Comput. Levon Nurbekyan, Alexander Iannantuono, Adam M. Oberman
2020	—	`ch.`	Partial differential equation regularization for supervised machine learning. 75 Years of Mathematics of Computation Adam M. Oberman
2019	J	`jnl`	A Partial Differential Equation Obstacle Problem for the Level Set Approach to Visibility. CoRR Adam M. Oberman, Tiago Salvador
2019	J	`jnl`	A principled approach for generating adversarial images under non-smooth dissimilarity metrics. CoRR Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman
2019	J	`jnl`	Empirical confidence estimates for classification by deep neural networks. CoRR Chris Finlay, Adam M. Oberman
2019	J	`jnl`	Farkas layers: don't shift the data, fix the geometry. CoRR Aram-Alexandre Pooladian, Chris Finlay, Adam M. Oberman
2019	J	`jnl`	Improved Accuracy of Monotone Finite Difference Schemes on Point Clouds and Regular Grids. SIAM J. Sci. Comput. Chris Finlay, Adam M. Oberman
2019	J	`jnl`	Partial differential equation regularization for supervised machine learning. CoRR Adam M. Oberman
2019	J	`jnl`	Scaleable input gradient regularization for adversarial robustness. CoRR Chris Finlay, Adam M. Oberman
2019	A*	`conf`	The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information. ICCV Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman
2019	J	`jnl`	The LogBarrier adversarial attack: making effective use of decision boundary information. CoRR Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman
2018	J	`jnl`	Anomaly Detection and Classification for Streaming Data using PDEs. SIAM J. Appl. Math. Bilal Abbasi, Jeff Calder, Adam M. Oberman
2018	J	`jnl`	Approximate Homogenization of Fully Nonlinear Elliptic PDEs: Estimates and Numerical Results for Pucci Type Equations. J. Sci. Comput. Chris Finlay, Adam M. Oberman
2018	J	`jnl`	Computing the Level Set Convex Hull. J. Sci. Comput. Bilal Abbasi, Adam M. Oberman
2018	J	`jnl`	Lipschitz regularized Deep Neural Networks converge and generalize. CoRR Adam M. Oberman, Jeff Calder
2018	J	`jnl`	Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-Means Clustering. J. Sci. Comput. Penghang Yin, Minh Pham, Adam M. Oberman, Stanley J. Osher
2017	J	`jnl`	A multigrid scheme for 3D Monge-Ampère equations. Int. J. Comput. Math. Jun Liu, Brittany D. Froese, Adam M. Oberman, Mingqing Xiao
2017	J	`jnl`	Approximate Convex Hulls. CoRR Robert Graham, Adam M. Oberman
2017	J	`jnl`	Deep Relaxation: partial differential equations for optimizing deep neural networks. CoRR Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier
2017	Misc	`conf`	Partial differential equations for training deep neural networks. ACSSC Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier
2017	J	`jnl`	Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-means Clustering. CoRR Penghang Yin, Minh Pham, Adam M. Oberman, Stanley J. Osher
2016	J	`jnl`	Adaptive Finite Difference Methods for Nonlinear Elliptic and Parabolic Partial Differential Equations with Free Boundaries. J. Sci. Comput. Adam M. Oberman, Ian Zwiers
2016	J	`jnl`	Anomaly detection and classification for streaming data using partial differential equations. CoRR Bilal Abbasi, Jeff Calder, Adam M. Oberman
2015	J	`jnl`	Filtered schemes for Hamilton-Jacobi equations: A simple construction of convergent accurate difference schemes. J. Comput. Phys. Adam M. Oberman, Tiago Salvador
2014	J	`jnl`	A multigrid solver for the three dimensional Monge-Ampère equation. CoRR Jun Liu, Brittany D. Froese, Adam M. Oberman, Mingqing Xiao
2014	J	`jnl`	Numerical Methods for the Fractional Laplacian: A Finite Difference-Quadrature Approach. SIAM J. Numer. Anal. Yanghong Huang, Adam M. Oberman
2014	J	`jnl`	Numerical solution of the Optimal Transportation problem using the Monge-Ampère equation. J. Comput. Phys. Jean-David Benamou, Brittany D. Froese, Adam M. Oberman
2013	J	`jnl`	A Numerical Method for Variational Problems with Convexity Constraints. SIAM J. Sci. Comput. Adam M. Oberman
2013	J	`jnl`	Convergent Filtered Schemes for the Monge-Ampère Partial Differential Equation. SIAM J. Numer. Anal. Brittany D. Froese, Adam M. Oberman
2013	J	`jnl`	Finite difference methods for the Infinity Laplace and J. Comput. Appl. Math. Adam M. Oberman
2011	J	`jnl`	Convergent Finite Difference Solvers for Viscosity Solutions of the Elliptic Monge-Ampère Equation in Dimensions Two and Higher. SIAM J. Numer. Anal. Brittany D. Froese, Adam M. Oberman
2011	J	`jnl`	Fast finite difference solvers for singular solutions of the elliptic Monge-Ampère equation. J. Comput. Phys. Brittany D. Froese, Adam M. Oberman
2010	J	`jnl`	Fast finite difference solvers for singular solutions of the elliptic Monge-Ampére equation CoRR Brittany D. Froese, Adam M. Oberman
2009	J	`jnl`	Homogenization of Metric Hamilton-Jacobi Equations. Multiscale Model. Simul. Adam M. Oberman, Ryo Takei, Alexander Vladimirsky
2006	J	`jnl`	Convergent Difference Schemes for Degenerate Elliptic and Parabolic Equations: Hamilton-Jacobi Equations and Free Boundary Problems. SIAM J. Numer. Anal. Adam M. Oberman
2005	J	`jnl`	A convergent difference scheme for the infinity Laplacian: construction of absolutely minimizing Lipschitz extensions. Math. Comput. Adam M. Oberman
2005	—	`conf`	Computing the Effective Hamiltonian using a Variational Approach. CDC/ECC Diogo A. Gomes, Adam M. Oberman
2004	J	`jnl`	A convergent monotone difference scheme for motion of level sets by mean curvature. Numerische Mathematik Adam M. Oberman
2004	J	`jnl`	Computing the Effective Hamiltonian Using a Variational Approach. SIAM J. Control. Optim. Diogo A. Gomes, Adam M. Oberman
2003	J	`jnl`	Pricing early exercise contracts in incomplete markets. Comput. Manag. Sci. Adam M. Oberman, Thaleia Zariphopoulou