Sat 6:50 a.m. - 7:00 a.m.
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Introducing the Optimal Transport and Machine Learning (OTML) Workshop
(
Introduction
)
>
SlidesLive Video
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🔗
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Sat 7:00 a.m. - 8:00 a.m.
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The making of the JKO scheme (Felix Otto)
(
Plenary talk
)
>
SlidesLive Video
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🔗
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Sat 8:00 a.m. - 8:30 a.m.
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Coffee break
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🔗
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Sat 8:30 a.m. - 9:00 a.m.
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Unbalanced Optimal Transport: Efficient solutions for outlier-robust machine learning (Laetitia Chapel)
(
Keynote talk
)
>
SlidesLive Video
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🔗
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Sat 9:00 a.m. - 9:15 a.m.
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Self-Supervised Learning with the Matching Gap (Zoe Piran)
(
Contributing talk
)
>
SlidesLive Video
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🔗
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Sat 9:15 a.m. - 9:30 a.m.
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Accelerating Motion Planning via Optimal Transport
(
Oral
)
>
link
SlidesLive Video
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An T. Le · Georgia Chalvatzaki · Armin Biess · Jan Peters
🔗
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Sat 9:30 a.m. - 10:00 a.m.
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Learning on graphs with Gromov-Wasserstein: from unsupervised learning to GNN (Rémi Flamary)
(
Keynote talk
)
>
SlidesLive Video
|
🔗
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Sat 10:00 a.m. - 11:30 a.m.
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Lunch + Poster Session I
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🔗
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Sat 11:30 a.m. - 12:00 p.m.
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Amortized optimization for optimal transport (Brandon Amos)
(
Keynote talk
)
>
SlidesLive Video
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🔗
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Sat 12:00 p.m. - 12:30 p.m.
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Diffusion Schrodinger Bridge Matching (Arnaud Doucet)
(
Keynote talk
)
>
SlidesLive Video
|
🔗
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Sat 12:30 p.m. - 1:00 p.m.
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Sketched Wasserstein Distances (Florentina Bunea)
(
Keynote talk
)
>
SlidesLive Video
|
🔗
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Sat 1:00 p.m. - 1:15 p.m.
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Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
(
Oral
)
>
link
SlidesLive Video
|
Aniket Das · Dheeraj Nagaraj
🔗
|
Sat 1:15 p.m. - 1:30 p.m.
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Towards a Statistical Theory of Learning to Learn In-context with Transformers
(
Oral
)
>
link
SlidesLive Video
|
Youssef Mroueh
🔗
|
Sat 1:30 p.m. - 2:00 p.m.
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Coffee break
|
🔗
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Sat 2:00 p.m. - 2:30 p.m.
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Optimal transport on graphs, manifolds and trees (Smita Krishnaswamy)
(
Keynote talk
)
>
SlidesLive Video
|
🔗
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Sat 2:30 p.m. - 3:00 p.m.
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Variational inference via Wasserstein gradient flows (Sinho Chewi)
(
Keynote talk
)
>
SlidesLive Video
|
🔗
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Sat 3:00 p.m. - 3:15 p.m.
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Closing remarks from organizers
(
Closing remarks
)
>
SlidesLive Video
|
🔗
|
Sat 3:15 p.m. - 3:30 p.m.
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Poster session II + hangout
(
Poster session
)
>
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🔗
|
-
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Network Regression with Wasserstein Distances
(
Poster
)
>
link
|
Alexander Zalles · Cesar Uribe · Kai M. Hung · Ann Finneran · Lydia Beaudrot
🔗
|
-
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Learning via Wasserstein-Based High Probability Generalisation Bounds
(
Poster
)
>
link
|
Paul Viallard · Maxime Haddouche · Umut Simsekli · Benjamin Guedj
🔗
|
-
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Improved Stein Variational Gradient Descent with Importance Weights
(
Poster
)
>
link
|
Lukang Sun · Peter Richtarik
🔗
|
-
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On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
(
Poster
)
>
link
|
Mathieu Serrurier · Franck Mamalet · Thomas FEL · Louis Béthune · Thibaut Boissin
🔗
|
-
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Entropic Gromov-Wasserstein Distances: Stability and Algorithms
(
Poster
)
>
link
|
Gabriel Rioux · Ziv Goldfeld · Kengo Kato
🔗
|
-
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SpecTr++: Improved transport plans for speculative decoding of large language models
(
Poster
)
>
link
|
Kwangjun Ahn · Ahmad Beirami · Ziteng Sun · Ananda Theertha Suresh
🔗
|
-
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Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
(
Poster
)
>
link
|
Kai Yan · Alex Schwing · Yu-Xiong Wang
🔗
|
-
|
Semi-discrete Gromov-Wasserstein distances: Existence of Gromov-Monge Maps and Statistical Theory
(
Poster
)
>
link
|
Gabriel Rioux · Ziv Goldfeld · Kengo Kato
🔗
|
-
|
Sliced Wasserstein Estimation with Control Variates
(
Poster
)
>
link
|
Khai Nguyen · Nhat Ho
🔗
|
-
|
Zero-shot Cross-task Preference Alignment for Offline RL via Optimal Transport
(
Poster
)
>
link
|
Runze Liu · Yali Du · Fengshuo Bai · Jiafei Lyu · Xiu Li
🔗
|
-
|
Accelerating Motion Planning via Optimal Transport
(
Poster
)
>
link
|
An T. Le · Georgia Chalvatzaki · Armin Biess · Jan Peters
🔗
|
-
|
Outlier-Robust Wasserstein DRO
(
Poster
)
>
link
|
Sloan Nietert · Ziv Goldfeld · Soroosh Shafiee
🔗
|
-
|
Towards a Statistical Theory of Learning to Learn In-context with Transformers
(
Poster
)
>
link
|
Youssef Mroueh
🔗
|
-
|
Estimating Fréchet bounds for validating programmatic weak supervision
(
Poster
)
>
link
|
Felipe Maia Polo · Mikhail Yurochkin · Moulinath Banerjee · Subha Maity · Yuekai Sun
🔗
|
-
|
Semidefinite Relaxations of the Gromov-Wasserstein Distance
(
Poster
)
>
link
|
Junyu Chen · Binh T. Nguyen · Yong Sheng Soh
🔗
|
-
|
Invertible normalizing flow neural networks by JKO scheme
(
Poster
)
>
link
|
Chen Xu · Xiuyuan Cheng · Yao Xie
🔗
|
-
|
SyMOT-Flow: Learning optimal transport flow for two arbitrary distributions with maximum mean discrepancy
(
Poster
)
>
link
|
Zhe Xiong · Qiaoqiao Ding · Xiaoqun Zhang
🔗
|
-
|
Computing high-dimensional optimal transport by flow neural networks
(
Poster
)
>
link
|
Chen Xu · Xiuyuan Cheng · Yao Xie
🔗
|
-
|
Duality and Sample Complexity for the Gromov-Wasserstein Distance
(
Poster
)
>
link
|
Zhengxin Zhang · Ziv Goldfeld · Youssef Mroueh · Bharath Sriperumbudur
🔗
|
-
|
PTLP: Partial Transport $L^p$ Distances
(
Poster
)
>
link
|
Xinran Liu · Yikun Bai · Huy Tran · Zhanqi Zhu · Matthew Thorpe · Soheil Kolouri
🔗
|
-
|
Optimal Transport for Measures with Noisy Tree Metric
(
Poster
)
>
link
|
Tam Le · Truyen Nguyen · Kenji Fukumizu
🔗
|
-
|
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
(
Poster
)
>
link
|
Kweku Kwegyir-Aggrey · Jessica Dai · A. Feder Cooper · John Dickerson · Keegan Hines · Suresh Venkatasubramanian
🔗
|
-
|
Causal Discovery via Monotone Triangular Transport Maps
(
Poster
)
>
link
|
Sina Akbari · Luca Ganassali · Negar Kiyavash
🔗
|
-
|
Applications of Optimal Transport Distances in Unsupervised AutoML
(
Poster
)
>
link
|
prabhant singh · Joaquin Vanschoren
🔗
|
-
|
Data-Conditional Diffusion Bridges
(
Poster
)
>
link
|
Ella Tamir · Martin Trapp · Arno Solin
🔗
|
-
|
Characterizing Out-of-Distribution Error via Optimal Transport
(
Poster
)
>
link
|
Yuzhe Lu · Yilong Qin · Runtian Zhai · Andrew Shen · Ketong Chen · Zhenlin Wang · Soheil Kolouri · Simon Stepputtis · Joseph Campbell · Katia Sycara
🔗
|
-
|
A generative flow model for conditional sampling via optimal transport
(
Poster
)
>
link
|
Jason Alfonso · Ricardo Baptista · Anupam Bhakta · Noam Gal · Alfin Hou · Vasilisa Lyubimova · Daniel Pocklington · Josef Sajonz · Giulio Trigila · Ryan Tsai
🔗
|
-
|
Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning
(
Poster
)
>
link
|
Ali Baheri
🔗
|
-
|
Optimal Transport with Adaptive Regularisation
(
Poster
)
>
link
|
Hugues Van Assel · Titouan Vayer · Rémi Flamary · Nicolas Courty
🔗
|
-
|
SpecTr: Fast Speculative Decoding via Optimal Transport
(
Poster
)
>
link
|
Ziteng Sun · Ananda Theertha Suresh · Jae Hun Ro · Ahmad Beirami · Himanshu Jain · Felix Yu
🔗
|
-
|
Quantum Theory and Application of Contextual Optimal Transport
(
Poster
)
>
link
|
Nicola Mariella · Jannis Born · Albert Akhriev · Francesco Tacchino · Christa Zoufal · Eugene Koskin · Ivano Tavernelli · Stefan Woerner · Maria Anna Rapsomaniki · Sergiy Zhuk
🔗
|
-
|
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
(
Poster
)
>
link
|
Hugues Van Assel · Cédric Vincent-Cuaz · Titouan Vayer · Rémi Flamary · Nicolas Courty
🔗
|
-
|
On Schrödinger Bridge Matching and Expectation Maximization
(
Poster
)
>
link
|
Rob Brekelmans · Kirill Neklyudov
🔗
|
-
|
Optimal transport for vector Gaussian mixture models
(
Poster
)
>
link
|
Jiening Zhu · Kaiming Xu · Allen Tannenbaum
🔗
|
-
|
Learning TSP Algorithmic Prior using Gumbel-Sinkhorn Operator
(
Poster
)
>
link
|
Yimeng Min · Carla Gomes
🔗
|
-
|
Adaptive Algorithms for Continuous-Time Transport: Homotopy-Driven Sampling and a New Interacting Particle System
(
Poster
)
>
link
|
Aimee Maurais · Youssef Marzouk
🔗
|
-
|
Fast and Accurate Cost-Scaling Algorithm for the Semi-Discrete Optimal Transport
(
Poster
)
>
link
|
Pankaj Agarwal · Sharath Raghvendra · Pouyan Shirzadian · Keegan Yao
🔗
|
-
|
A Computational Framework for Solving Wasserstein Lagrangian Flows
(
Poster
)
>
link
|
Kirill Neklyudov · Rob Brekelmans · Alexander Tong · Lazar Atanackovic · Qiang Liu · Alireza Makhzani
🔗
|
-
|
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
(
Poster
)
>
link
|
Aniket Das · Dheeraj Nagaraj
🔗
|
-
|
Fourier-Based Bounds for Wasserstein Distances and Their Implications in Computational Inversion
(
Poster
)
>
link
|
Wanli Hong · Vlad Kobzar · Kui Ren
🔗
|