OTML Workshop Schedule


All oral presentations are available on SlideLive:

We want to thank all the speakers and participants.

Accepted Posters

  • Andrew Carr, Jared Nielson and David Wingate, Wasserstein Neural Processes
  • Quentin Berthet and Jonathan Niles-Weed, Estimation of smooth densities in Wasserstein distance
  • Wenlin Wang, Hongteng Xu, Guoyin Wang, Wenqi Wang, Hao Wang, Bangjie Liu and Lawrence Carin, Improving Zero-Shot Learning via Optimal Transport
  • David Alvarez-Melis, Youssef Mroueh and Tommi Jaakkola, Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic spaces
  • Georgios Balikas and Ioannis Partalas, Wasserstein distances for evaluating cross-lingual embeddings
  • Rémi Flamary, Karim Lounici and André Ferrari, Concentration bounds for linear Monge mapping estimation
  • Dirk Lorenz and Hinrich Mahler, Orlicz-space regularization for optimal transport and algorithms for quadratic regularization
  • Elsa Cazelles, Arnaud Robert and Felipe Tobar, The Wasserstein Distance for Time Series Analysis in the Fourier Domain
  • John Lee, Nicholas Bertrand and Christopher Rozell, Parallel Unbalanced Optimal Transport Regularization for Imaging
  • John Lee, Max Dabagia, Eva Dyer and Christopher Rozell, Hierarchical Optimal Transport for Multimodal Distribution Alignment
  • John Lee and Christopher Rozell, A General ADMM Framework for Optimal Transport Regularized Problems
  • Lingxuan Shao, Fang Yao and Chunlin Ji, Linear Regression under Wasserstein Distance
  • Ashok Makkuva, Amirhossein Taghvaei, Sewoong Oh and Jason Lee, Optimal transport mapping via input convex neural networks
  • Esteban G. Tabak, Giulio Trigila and Wenjun Zhao, Data Driven Conditional Optimal Transport
  • Thibault Séjourné, Jean Feydy, François-Xavier Vialard, Alain Trouvé and Gabriel Peyré, Sinkhorn Divergences for Unbalanced Optimal Transport
  • Youssef Mroueh, Wasserstein Style Transfer
  • Guillermo Ortiz-Jimenez, Mireille El Gheche, Effrosyni Simou, Hermina Petric Maretic and Pascal Frossard, CDOT: Continuous Domain Adaptation using Optimal Transport
  • Levon Nurbekyan, Alexander Iannantuono and Adam Oberman, No-collision Transportation Maps
  • Samuel Cohen and Dino Sejdinovic, On the Gromov-Wasserstein distance and Coupled Deep Generative Models
  • Christian Bock, Matteo Togninalli, Elisabetta Ghisu, Thomas Gumbsch, Bastian Rieck and Karsten Borgwardt, A Wasserstein Subsequence Kernel for Time Series
  • Anthony Tompkins, Ransalu Senanayake and Fabio Ramos, Parameter Optimal Transport for Robotic Mapping
  • Alex Lin, Yonatan Dukler, Wuchen Li and Guido Montufar, Wasserstein Diffusion Tikhonov Regularization
  • Sofien Dhouib and Ievgen Redko, On exploring the robust formulation of optimal transport
  • François-Pierre Paty, Alexandre d'Aspremont and Marco Cuturi, Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials
  • Konstantin Mishchenko, Sinkhorn Algorithm as a Special Case of Stochastic Mirror Descent
  • Max Dabagia and Eva Dyer, Barycenters in the brain: An optimal transport approach to modeling connectivity
  • Chaosheng Dong and Bo Zeng, Wasserstein Distributionally Robust Inverse Multiobjective Optimization
  • Liang Mi, Wen Zhang and Yalin Wang, Regularized Wasserstein Means for Aligning Distributional Data
  • Tudor Manole, Sivaraman Balakrishnan and Larry Wasserman, Minimax Confidence Intervals for the Sliced Wasserstein Distance
  • Kyle Swanson, Lili Yu and Tao Lei, Interpretable Text Matching by Learning a Constrained Alignment
  • Yihe Dong, Piotr Indyk, Ilya Razenshteyn and Tal Wagner, Scalable Nearest Neighbor Search for Optimal Transport
  • Mukul Bhutani, Thomas Magelinski and Zico Kolter, Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport
  • Yihe Dong, Yu Gao, Richard Peng, Ilya Razenshteyn and Saurabh Sawlani, A Study of Performances of Optimal Transport
  • Zihao Wang, Yong Zhang and Hao Wu, Cosine Sentence Similarity Meets Optimal Transport
  • Pierre Bréchet, Tao Wu, Thomas Möllenhoff and Daniel Cremers, Informative GANs via Structured Regularization of Optimal Transport
  • Sidak Pal Singh and Martin Jaggi, Structure-Aware Model Averaging via Optimal Transport
  • Etienne Boursier and Vianney Perchet, Trade-off privacy/utility through the lens of optimal transport
  • Alex Delalande and Quentin Mérigot, Linearization of the 2-Wasserstein space and stability of optimal transport maps