Aram-Alexandre Pooladian

According to our database, Aram-Alexandre Pooladian authored at least 17 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps.

Ricardo Baptista, Aram-Alexandre Pooladian, Michael Brennan, Youssef Marzouk, Jonathan Niles-Weed

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Wasserstein Flow Matching: Generative modeling over families of distributions.

Doron Haviv, Aram-Alexandre Pooladian, Dana Pe'er, Brandon Amos

CoRR, 2024

Plug-in estimation of Schrödinger bridges.

Aram-Alexandre Pooladian, Jonathan Niles-Weed

CoRR, 2024

Neural Optimal Transport with Lagrangian Costs.

Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos

Proceedings of the Uncertainty in Artificial Intelligence, 2024

Learning Elastic Costs to Shape Monge Displacements.

Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Progressive Entropic Optimal Transport Solvers.

Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space.

Yiheng Jiang, Sinho Chewi, Aram-Alexandre Pooladian

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow.

Carles Domingo-Enrich, Aram-Alexandre Pooladian

Trans. Mach. Learn. Res., 2023

Learning Costs for Structured Monge Displacements.

Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi

CoRR, 2023

Minimax estimation of discontinuous optimal transport maps: The semi-discrete case.

Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed

Proceedings of the International Conference on Machine Learning, 2023

Multisample Flow Matching: Straightening Flows with Minibatch Couplings.

Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen

Proceedings of the International Conference on Machine Learning, 2023

2022

A study of one-parameter regularization methods for mathematical programs with vanishing constraints.

Tim Hoheisel, Blanca Pablos, Aram-Alexandre Pooladian, Alexandra Schwartz, Luke Steverango

Optim. Methods Softw., 2022

Debiaser Beware: Pitfalls of Centering Regularized Transport Maps.

Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed

Proceedings of the International Conference on Machine Learning, 2022

2020

Learning normalizing flows from Entropy-Kantorovich potentials.

Chris Finlay, Augusto Gerolin, Adam M. Oberman, Aram-Alexandre Pooladian

CoRR, 2020

A principled approach for generating adversarial images under non-smooth dissimilarity metrics.

Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Farkas layers: don't shift the data, fix the geometry.

Aram-Alexandre Pooladian, Chris Finlay, Adam M. Oberman

CoRR, 2019

The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information.

Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019