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Workshop
Mon Dec 13 05:00 AM -- 01:00 PM (PST)
Optimal Transport and Machine Learning
Jason Altschuler · Charlotte Bunne · Laetitia Chapel · Marco Cuturi · Rémi Flamary · Gabriel Peyré · Alexandra Suvorikova





Over the last few years, optimal transport (OT) has quickly become a central topic in machine learning. OT is now routinely used in many areas of ML, ranging from the theoretical use of OT flow for controlling learning algorithms to the inference of high-dimensional cell trajectories in genomics. The Optimal Transport and Machine Learning (OTML) workshop series (in '14, '17, '19) has been instrumental in shaping this research thread. For this new installment of OTML, we aim even bigger by hosting an exceptional keynote speaker, Alessio Figalli, who received the 2018 Fields Medal for his breakthroughs in the analysis of the regularity properties of OT. OTML will be a unique opportunity for cross-fertilization between recent advances in pure mathematics and challenging high-dimensional learning problems.

Optimal Transport in the Biomedical Sciences: Challenges and Opportunities (Plenary talk)
Implicit Riemannian Concave Potential Maps (Oral)
Regularity theory of optimal transport maps (Plenary talk)
Generative adversarial learning with adapted distances (Keynote talk)
Spotlight Presentations
Poster Session
Entropic Regularization of Optimal Transport as a Statistical Regularization (Plenary talk)
Optimal transport and probability flows (Keynote talk)
Graphical Optimal Transport and its applications (Keynote talk)
Poster Session
Enabling integrated analysis of single-cell multi-omic datasets with optimal transport (Keynote talk)
Entropic estimation of optimal transport maps (Oral)
Discrete Schrödinger Bridges with Applications to Two-Sample Homogeneity Testing (Oral)
Benefits of using optimal transport in computational learning and inversion (Keynote talk)
Concluding Remarks (Discussion)
Poster session - 3 and social interaction (Poster session)
Sinkhorn EM: An Expectation-Maximizationalgorithm based on entropic optimal transport (Spotlight)
Discrete Schrödinger Bridges with Applications to Two-Sample Homogeneity Testing (Oral)
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport (Poster)
Factored couplings in multi-marginal optimal transport via difference of convex programming (Poster)
Subspace Detours Meet Gromov-Wasserstein (Poster)
Towards interpretable contrastive word mover's embedding (Poster)
Implicit Riemannian Concave Potential Maps (Oral)
Efficient estimates of optimal transport via low-dimensional embeddings (Poster)
A Central Limit Theorems for Multidimensional Wasserstein Distances (Poster)
Sliced Multi-Marginal Optimal Transport (Poster)
Learning Single-Cell Perturbation Responses using Neural Optimal Transport (Poster)
Entropic estimation of optimal transport maps (Oral)
Towards an FFT for measures (Poster)
Factored couplings in multi-marginal optimal transport via difference of convex programming (Spotlight)
Subspace Detours Meet Gromov-Wasserstein (Spotlight)
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Network (Poster)
Entropic estimation of optimal transport maps (Poster)
Learning Revenue-Maximizing Auctions With Differentiable Matching (Poster)
Measuring association with Wasserstein distances (Poster)
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe (Poster)
Input Convex Gradient Networks (Poster)
Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport (Poster)
Wasserstein Adversarially Regularized Graph Autoencoder (Poster)
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Network (Spotlight)
Implicit Riemannian Concave Potential Maps (Poster)
Learning Revenue-Maximizing Auctions With Differentiable Matching (Spotlight)
Input Convex Gradient Networks (Spotlight)
Discrete Schrödinger Bridges with Applications to Two-Sample Homogeneity Testing (Poster)
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs (Spotlight)
Dual Regularized Optimal Transport (Poster)
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs (Poster)
Optimal Transport losses and Sinkhorn algorithm with general convex regularization (Poster)
Likelihood Training of Schrödinger Bridges using Forward-Backward SDEs Theory (Poster)
Multistage Monge Kantorovich Problem applied to optimal ecological transition (Poster)
Variational Wasserstein gradient flow (Poster)
On the complexity of the optimal transport problem with graph-structured cost (Poster)
Gradient flows on graphons: existence, convergence, continuity equations (Poster)
Linear Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport (Poster)
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe (Spotlight)
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck (Poster)