Optimal Transport and Machine Learning

List of Accepted Papers

Orals

• Discrete Schrödinger Bridges with Applications to Two-Sample Homogeneity Testing. Zaid Harchaoui, Lang Liu, Soumik Pal
• Entropic estimation of optimal transport maps. Aram-Alexandre Pooladian, Jonathan Niles-Weed
• Implicit Riemannian Concave Potential Maps. Danilo Jimenez Rezende, Sebastien Racaniere

Spotlights

• Optimizing Functionals on the Space of Probabilities with Input Convex Neural Network. David Alvarez-Melis, Yair Schiff, Youssef Mroueh
• Learning Revenue-Maximizing Auctions With Differentiable Matching. Michael Curry, Uro Cornelius Lyi, Tom Goldstein, John P Dickerson
• Factored couplings in multi-marginal optimal transport via difference of convex programming. Quang Huy Tran, Hicham Janati, Ievgen Redko, Rémi Flamary, Nicolas Courty
• Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport. Gonzalo E. Mena, Amin Nejatbakhsh, Erdem Varol, Jonathan Niles-Weed
• Subspace Detours Meet Gromov-Wasserstein. Clément Bonet, Nicolas Courty, Fançois Septier, Lucas Drumetz
• Input Convex Gradient Networks. Jack Richter-Powell, Jonathan Peter Lorraine, Brandon Amos
• Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs. Meyer Scetbon, Gabriel Peyré, Marco Cuturi
• Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe. Thibault Sejourne, François-Xavier Vialard, Gabriel Peyré

Posters

• Optimal Transport losses and Sinkhorn algorithm with general convex regularization. Augusto Gerolin, Simone Di Marino.
• Multistage Monge Kantorovich Problem applied to optimal ecological transition. Clara Macedo Lage, Emmanuel Gobet.
• Measuring association with Wasserstein distances. Johannes Wiesel
• Dual Regularized Optimal Transport. Rishi Sonthalia, Anna Gilbert
• Towards interpretable contrastive word mover’s embedding. Ruijie Jiang, Julia Gouvea, Eric Miller, David Hammer, Shuchin Aeron
• Efficient estimates of optimal transport via low-dimensional embeddings. Patric Fulop, Vincent Danos
• Likelihood Training of Schrödinger Bridges using Forward-Backward SDEs Theory. Tianrong Chen, Guan-Horng Liu, Evangelos Theodorou
• Wasserstein Adversarially Regularized Graph Autoencoder. Huidong Liang, Junbin Gao
• Sliced Multi-Marginal Optimal Transport. Samuel Cohen, Alexander Terenin, Yannik Pitcan, Brandon Amos, Marc Peter Deisenroth, K S Sesh Kumar
• On Combining Expert Demonstrations in Imitation Learning via Optimal Transport. Ilana Sebag, Samuel Cohen, Marc Peter Deisenroth
• Towards an FFT for measures. Paul Catala, Mathias Hockmann, Stefan Kunis, Markus Wageringel
• Variational Wasserstein gradient flow. Jiaojiao Fan, Amirhossein Taghvaei, Yongxin Chen
• A Central Limit Theorem for Semidiscrete Wasserstein Distances. Alberto Gonzalez Sanz, Loubes Jean-Michel, Eustasio del Barrio
• On the complexity of the optimal transport problem with graph-structured cost. Jiaojiao Fan, Isabel Haasler, Johan Karlsson, Yongxin Chen
• Learning Single-Cell Perturbation Responses using Neural Optimal Transport. Charlotte Bunne, Stefan Stark, Gabriele Gut, Andreas Krause, Gunnar Ratsch, Lucas Pelkmans, Kjong Lehmann
• Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck. Huan Liu, George Zhang, Jun Chen, Ashish J. Khisti
• Gradient flows on graphons: existence, convergence, continuity equations. Sewoong Oh, Soumik Pal, Raghav Somani, Raghav Tripathi
• Linear Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport. Vladimir R Kostic, Saverio Salzo, Massimiliano Pontil