Preprints and Submissions

1.

Near-Lipschitz stability of the Kim--Milman flow map

Chewi, S., Eichinger, K., and Pooladian, A-A. (alphabetical)

arXivPDF
2.

Blind denoising diffusion models and the blessings of dimensionality

Kadkhodaie, Z.*, Pooladian, A-A.*, Chewi, S., and Simoncelli, E. (Joint first author)

Spotlight at FoGEN ICML workshop; 2026

arXivPDFTalk
3.

Theory and computation for structured variational inference

Sheng, S., Wu, B., Zhu, B. Chewi, S., and Pooladian, A-A.

arXivPDF
4.

Stability of the Kim-Milman flow map

Chewi, S., Pooladian, A-A., and Zhang, M. (alphabetical)

arXivPDFBibTeX
5.

Entropic estimation of optimal transport maps

Pooladian, A-A., and Niles-Weed, J.

Best paper award at OTML NeurIPS workshop; 2021

arXivPDFCodeBibTeX

Journal Articles

1.

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

Jiang, Y., Chewi, S., and Pooladian, A-A. (Supervisory role)

Foundations of Computational Mathematics (2025+)

arXivPDFSlidesCodeBibTeX
2.

Plug-in estimation of Schrödinger bridges

Pooladian, A-A., and Niles-Weed, J.

SIAM Journal on Mathematics of Data Science (2025+)

arXivPDFCodeBibTeX
3.

Tight stability for entropic Brenier maps

Divol, V., Niles-Weed, J., and Pooladian, A-A. (alphabetical)

International Mathematics Research Notices (2025+)

arXivPDFBibTeX
4.

Optimal transport map estimation in general function spaces

Divol, V., Niles-Weed, J., and Pooladian, A-A. (alphabetical)

Annals of Statistics (2025+)

arXivPDFSlidesBibTeX
5.

An entropic generalization of Caffarelli's contraction theorem via covariance inequalities

Chewi, S., and Pooladian, A-A. (alphabetical)

Comptes Rendus Mathématique (2023)

arXivPDFSlidesBibTeX
6.

An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao gradient flow

Domingo-Enrich, C., and Pooladian, A-A. (alphabetical)

Transactions on Machine Learning Research (2023)

arXivPDFBibTeX

Conference Papers

1.

Variational inference via radial transport

Ghafourpour, L., Chewi, S., Figalli, A., and Pooladian, A-A. (supervisory role)

Twenty-Ninth Annual Conference on Artificial Intelligence and Statistics (AISTATS 2026)

arXivPDFCode
2.

Wasserstein Flow Matching: Generative modeling over families of distributions

Haviv, D.*, Pooladian, A-A.*, Pe'er, D., and Amos, B. (Joint first author)

41st International Conference on Machine Learning (ICML 2025)

arXivPDFCodeBibTeX
3.

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

Baptista, R.*, Pooladian, A-A.*, Brennan, M., Marzouk Y., and Niles-Weed, J. (Joint first author)

28th International Conference on Artificial Intelligence and Statistics (AISTATS 2025)

arXivPDFCodeBibTeX
4.

Progressive Entropic Optimal Transport Solvers

Kassraie, P., Pooladian, A-A., Klein, M., Thornton, J., Niles-Weed, J., and Cuturi, M.

38th Conference on Neural Information Processing Systems (NeurIPS 2024)

arXivPDFBibTeX
5.

Learning Costs for Structured Monge Displacements

Klein, M., Pooladian, A-A., Ablin, P., Ndiaye, E., Niles-Weed, J., and Cuturi, M.

38th Conference on Neural Information Processing Systems (NeurIPS 2024)

arXivPDFBibTeX
6.

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

Jiang, Y., Chewi, S., and Pooladian, A-A. (Supervisory role)

Extended abstract in the 37th Conference on Learning Theory (COLT 2024)

arXivPDFSlidesCodeBibTeX
7.

Neural Optimal Transport with Lagrangian Costs

Pooladian, A-A., Domingo-Enrich, C., Chen R., and Amos, B.

40th International Conference on Uncertainty in Artificial Intelligence (UAI 2024)

arXivPDFCodeBibTeX
8.

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

Pooladian, A-A.*, Divol, V.*, and Niles-Weed, J. (Joint first author)

40th International Conference on Machine Learning (ICML 2023)

arXivPDFSlidesBibTeX
9.

Multisample Flow Matching: Straightening Flows with Minibatch Couplings

Pooladian, A-A.*, Ben-Hamu, H.*, Domingo-Enrich, C.*, Amos, B., Lipman, Y., and Chen, R. (Joint first author)

40th International Conference on Machine Learning (ICML 2023)

arXivPDFBibTeX
10.

Debiaser Beware: Pitfalls of Centering Regularized Transport maps

Pooladian, A-A., Cuturi, M., and Niles-Weed, J.

39th International Conference on Machine Learning (ICML 2022)

arXivPDFSlidesCodeBibTeX

Pre-PhD Conference, Journal, and Workshop Papers

1.

Learning normalizing flows from Entropy-Kantorovich potentials

Finlay, C., Gerolin, A., Oberman, A., Pooladian A-A. (alphabetical)

Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020 workshop), with contributing talk

arXivPDFBibTeX
2.

A study of one-parameter regularizations for mathematical programs with vanishing constraints

Hoheisel, T., Pablos, B., Pooladian, A-A., Schwartz, A., and Steverango, L. (alphabetical)

Optimization Methods and Software (2020)

arXivPDFBibTeX
3.

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

Pooladian, A-A., Finlay, C., Hoheisel, T., and Oberman, A.

23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)

arXivPDFBibTeX
4.

The LogBarrier Adversarial Attack: Making effective use of decision boundary information

Finlay, C., Pooladian, A-A., and Oberman, A.

IEEE International Conference on Computer Vision (ICCV 2019)

arXivPDFBibTeX