Pricing early exercise contracts in incomplete markets. - Adam M. Oberman; Thaleia Zariphopoulou (Comput. Manag. Sci., 2003)

A convergent monotone difference scheme for motion of level sets by mean curvature. - Adam M. Oberman (Numerische Mathematik, 2004)

Computing the Effective Hamiltonian Using a Variational Approach. - Diogo A. Gomes 002; Adam M. Oberman (SIAM J. Control. Optim., 2004)

A convergent difference scheme for the infinity Laplacian: construction of absolutely minimizing Lipschitz extensions. - Adam M. Oberman (Math. Comput., 2005)

Convergent Difference Schemes for Degenerate Elliptic and Parabolic Equations: Hamilton-Jacobi Equations and Free Boundary Problems. - Adam M. Oberman (SIAM J. Numer. Anal., 2006)

Homogenization of Metric Hamilton-Jacobi Equations. - Adam M. Oberman; Ryo Takei; Alexander Vladimirsky (Multiscale Model. Simul., 2009)

Fast finite difference solvers for singular solutions of the elliptic Monge-Ampère equation. - Brittany D. Froese; Adam M. Oberman (J. Comput. Phys., 2011)

Convergent Finite Difference Solvers for Viscosity Solutions of the Elliptic Monge-Ampère Equation in Dimensions Two and Higher. - Brittany D. Froese; Adam M. Oberman (SIAM J. Numer. Anal., 2011)

Finite difference methods for the Infinity Laplace and pp-Laplace equations. - Adam M. Oberman (J. Comput. Appl. Math., 2013)

Convergent Filtered Schemes for the Monge-Ampère Partial Differential Equation. - Brittany D. Froese; Adam M. Oberman (SIAM J. Numer. Anal., 2013)

A Numerical Method for Variational Problems with Convexity Constraints. - Adam M. Oberman (SIAM J. Sci. Comput., 2013)

A multigrid solver for the three dimensional Monge-Ampère equation. - Jun Liu 025; Brittany D. Froese; Adam M. Oberman; Mingqing Xiao 001 (CoRR, 2014)

Numerical solution of the Optimal Transportation problem using the Monge-Ampère equation. - Jean-David Benamou; Brittany D. Froese; Adam M. Oberman (J. Comput. Phys., 2014)

Numerical Methods for the Fractional Laplacian: A Finite Difference-Quadrature Approach. - Yanghong Huang; Adam M. Oberman (SIAM J. Numer. Anal., 2014)

Filtered schemes for Hamilton-Jacobi equations: A simple construction of convergent accurate difference schemes. - Adam M. Oberman; Tiago Salvador (J. Comput. Phys., 2015)

Anomaly detection and classification for streaming data using partial differential equations. - Bilal Abbasi; Jeff Calder; Adam M. Oberman (CoRR, 2016)

Adaptive Finite Difference Methods for Nonlinear Elliptic and Parabolic Partial Differential Equations with Free Boundaries. - Adam M. Oberman; Ian Zwiers (J. Sci. Comput., 2016)

Partial differential equations for training deep neural networks. - Pratik Chaudhari; Adam M. Oberman; Stanley J. Osher; Stefano Soatto; Guillaume Carlier (ACSSC, 2017)

Deep Relaxation: partial differential equations for optimizing deep neural networks. - Pratik Chaudhari; Adam M. Oberman; Stanley J. Osher; Stefano Soatto; Guillaume Carlier (CoRR, 2017)

Approximate Convex Hulls. - Robert Graham; Adam M. Oberman (CoRR, 2017)

A multigrid scheme for 3D Monge-Ampère equations. - Jun Liu 025; Brittany D. Froese; Adam M. Oberman; Mingqing Xiao 001 (Int. J. Comput. Math., 2017)

Lipschitz regularized Deep Neural Networks converge and generalize. - Adam M. Oberman; Jeff Calder (CoRR, 2018)

Computing the Level Set Convex Hull. - Bilal Abbasi; Adam M. Oberman (J. Sci. Comput., 2018)

Approximate Homogenization of Fully Nonlinear Elliptic PDEs: Estimates and Numerical Results for Pucci Type Equations. - Chris Finlay; Adam M. Oberman (J. Sci. Comput., 2018)

Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-Means Clustering. - Penghang Yin; Minh Pham 003; Adam M. Oberman; Stanley J. Osher (J. Sci. Comput., 2018)

Anomaly Detection and Classification for Streaming Data using PDEs. - Bilal Abbasi; Jeff Calder; Adam M. Oberman (SIAM J. Appl. Math., 2018)

The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information. - Chris Finlay; Aram-Alexandre Pooladian; Adam M. Oberman (ICCV, 2019)

Empirical confidence estimates for classification by deep neural networks. - Chris Finlay; Adam M. Oberman (CoRR, 2019)

Scaleable input gradient regularization for adversarial robustness. - Chris Finlay; Adam M. Oberman (CoRR, 2019)

Partial differential equation regularization for supervised machine learning. - Adam M. Oberman (CoRR, 2019)

Farkas layers: don't shift the data, fix the geometry. - Aram-Alexandre Pooladian; Chris Finlay; Adam M. Oberman (CoRR, 2019)

Improved Accuracy of Monotone Finite Difference Schemes on Point Clouds and Regular Grids. - Chris Finlay; Adam M. Oberman (SIAM J. Sci. Comput., 2019)

Partial differential equation regularization for supervised machine learning. - Adam M. Oberman (75 Years of Mathematics of Computation, 2020)

A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case. - Maxime Laborde; Adam M. Oberman (AISTATS, 2020)

A principled approach for generating adversarial images under non-smooth dissimilarity metrics. - Aram-Alexandre Pooladian; Chris Finlay; Tim Hoheisel; Adam M. Oberman (AISTATS, 2020)

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

How to train your neural ODE. - Chris Finlay; Jörn-Henrik Jacobsen; Levon Nurbekyan; Adam M. Oberman (CoRR, 2020)

Learning normalizing flows from Entropy-Kantorovich potentials. - Chris Finlay; Augusto Gerolin; Adam M. Oberman; Aram-Alexandre Pooladian (CoRR, 2020)

Deterministic Gaussian Averaged Neural Networks. - Ryan Campbell; Chris Finlay; Adam M. Oberman (CoRR, 2020)

Adversarial Boot Camp: label free certified robustness in one epoch. - Ryan Campbell; Chris Finlay; Adam M. Oberman (CoRR, 2020)

No-Collision Transportation Maps. - Levon Nurbekyan; Alexander Iannantuono; Adam M. Oberman (J. Sci. Comput., 2020)

A Partial Differential Equation Obstacle Problem for the Level Set Approach to Visibility. - Adam M. Oberman; Tiago Salvador (J. Sci. Comput., 2020)

Bias Mitigation of Face Recognition Models Through Calibration. - Tiago Salvador; Stephanie Cairns; Vikram Voleti; Noah Marshall; Adam M. Oberman (CoRR, 2021)

Improved Predictive Uncertainty using Corruption-based Calibration. - Tiago Salvador; Vikram Voleti; Alexander Iannantuono; Adam M. Oberman (CoRR, 2021)

Stochastic Gradient Descent with Polyak's Learning Rate. - Mariana Oliveira Prazeres; Adam M. Oberman (J. Sci. Comput., 2021)

On the Generalization of Representations in Reinforcement Learning. - Charline Le Lan; Stephen Tu; Adam Oberman; Rishabh Agarwal; Marc G. Bellemare (AISTATS, 2022)

FairCal: Fairness Calibration for Face Verification. - Tiago Salvador; Stephanie Cairns; Vikram Voleti; Noah Marshall; Adam M. Oberman (ICLR, 2022)

EuclidNets: Combining Hardware and Architecture Design for Efficient Training and Inference. - Mariana Oliveira Prazeres; Xinlin Li; Adam M. Oberman; Vahid Partovi Nia (ICPRAM, 2022)

Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models. - Vikram Voleti; Christopher Pal; Adam M. Oberman (CoRR, 2022)

Addressing Sample Inefficiency in Multi-View Representation Learning. - Kumar Krishna Agrawal; Arna Ghosh; Adam Oberman; Blake A. Richards (CoRR, 2023)

A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. - Tiago Salvador; Kilian Fatras; Ioannis Mitliagkas; Adam M. Oberman (Trans. Mach. Learn. Res., 2023)

EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models. - Xinlin Li; Mariana Parazeres; Adam Oberman; Alireza Ghaffari; Masoud Asgharian; Vahid Partovi Nia (SN Comput. Sci., 2023)

Harnessing small projectors and multiple views for efficient vision pretraining. - Arna Ghosh; Kumar Krishna Agrawal; Shagun Sodhani; Adam Oberman; Blake A. Richards (NeurIPS, 2024)

Multi-resolution continuous normalizing flows. - Vikram Voleti; Chris Finlay; Adam Oberman; Christopher Pal (Ann. Math. Artif. Intell., 2024)

Can Safety Fine-Tuning Be More Principled? Lessons Learned from Cybersecurity. - David Williams-King; Linh Le; Adam Oberman; Yoshua Bengio (CoRR, 2025)

Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path? - Yoshua Bengio; Michael K. Cohen; Damiano Fornasiere; Joumana Ghosn; Pietro Greiner; Matt MacDermott; Sören Mindermann; Adam Oberman; Jesse Richardson; Oliver Richardson; Marc-Antoine Rondeau; Pierre-Luc St-Charles; et al. (CoRR, 2025)

Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs. - Mehran Shakerinava; Siamak Ravanbakhsh; Adam Oberman (CoRR, 2025)

Numerical PDE solvers outperform neural PDE solvers. - Patrick Chatain; Michael Rizvi-Martel; Guillaume Rabusseau; Adam Oberman (CoRR, 2025)