Accepted Papers

Oral presentations:

  1. Mroueh, Y. Towards a Statistical Theory of Learning to Learn In-context with Transformers
  2. Le, A., Chalvatzaki, G., Biess, A., Peters, J. Accelerating Motion Planning via Optimal Transport
  3. Das, A., Nagaraj D. Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation


  1. Brekelmans, R., Neklyudov, K. On Schrödinger Bridge Matching and Expectation Maximization
  2. Hong, W., Kobzar, V., Ren, K. Fourier-Based Bounds for Wasserstein Distances and Their Implications in Computational Inversion
  3. Nietert, S., Goldfeld, Z., Shafieezadeh-Abadeh, S. Outlier-Robust Wasserstein DRO
  4. Zalles, A., Hung, K., Finneran, A., Beaudrot, L., Uribe, C. Network Regression with Wasserstein Distances
  5. Maurais, A., Marzouk Y. Adaptive Algorithms for Continuous-Time Transport: Homotopy-Driven Sampling and a New Interacting Particle System
  6. Le, T., Nguyen, T., Fukumizu, K. Optimal Transport for Measures with Noisy Tree Metric
  7. Singh, P., Vanschoren, J. Applications of Optimal Transport Distances in Unsupervised AutoML
  8. Min, Y., Gomes, C. Unsupervised Learning Permutations for TSP using Gumbel-Sinkhorn Operator
  9. Alfonso, J., Baptista, R., Bhakta, A., Gal, N., Hou, A., Lyubimova, V., Pocklington, D., Sajonz, J., Trigila, G., Tsai, R. A generative flow model for conditional sampling via optimal transport
  10. Polo, F., Yurochkin, M., Banerjee, M., Maity, S., Sun, Y. Estimating Fréchet bounds for validating programmatic weak supervision
  11. Neklyudov, K., Brekelmans, R., Tong, A., Atanackovic, L., Liu, Q., Makhzani, A. A Computational Framework for Solving Wasserstein Lagrangian Flows
  12. Kwegyir-Aggrey, K., Dai, J., Cooper, A., Dickerson, J., Hines, K., Venkatasubramanian, S. Repairing Regressors for Fair Binary Classification at Any Decision Threshold
  13. Akbari, S., Ganassali, L., Kiyavash, N. Causal Discovery via Monotone Triangular Transport Maps
  14. Assel, H., Vayer, T., Flamary, R., Courty, N. Optimal Transport with Adaptive Regularisation
  15. Mariella, N., Born, J., Akhriev, A., Tacchino, F., Zoufal, C., Koskin, E., Tavernelli, I., Woerner, S., Rapsomaniki, M., Zhuk, S. Quantum Theory and Application of Contextual Optimal Transport
  16. Viallard, P, Haddouche, M., Simsekli, U., Guedj, B. Learning via Wasserstein-Based High Probability Generalisation Bounds
  17. Chen, J., Nguyen, B., Soh, Y. Semidefinite Relaxations of the Gromov-Wasserstein Distance
  18. Agarwal, P., Raghvendra, S., Shirzadian, P., Yao, K. Fast and Accurate Cost-Scaling Algorithm for the Semi-Discrete Optimal Transport
  19. Zhu, J., Xu, K., Tannenbaum, A. Optimal transport for vector Gaussian mixture models
  20. Rioux, G., Goldfeld, Z., Kato, K. Semi-discrete Gromov-Wasserstein distances: Existence of Gromov-Monge Maps and Statistical Theory
  21. Rioux, G., Goldfeld, Z., Kato, K. Entropic Gromov-Wasserstein Distances: Stability and Algorithms
  22. Xu, C., Cheng, X., Xie, Y. Normalizing flow neural networks by JKO scheme
  23. Xu, C., Cheng, X., Xie, Y. Computing high-dimensional optimal transport by flow neural networks
  24. Ahn, K., Beirami, A., Sun, Z., Suresh, A. SpecTr++: Improved transport plans for speculative decoding of large language models
  25. Lu, Y., Qin, Y., Zhai, R., Shen, A., Chen, K., Wang, Z., Kolouri, S., Stepputtis, S., Campbell, J., Sycara, K. Characterizing Out-of-Distribution Error via Optimal Transport
  26. Assel, H., Vincent-Cuaz, C., Vayer, T., Flamary, R., Courty, N. Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
  27. Baheri, A. Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning
  28. Tamir, E., Trapp, M., Solin, A. Data-Conditional Diffusion Bridges
  29. Liu, R., Du, Y., Bai, F., Lyu, J., Li, X. Zero-shot Cross-task Preference Alignment for Offline RL via Optimal Transport
  30. Zhang, Z., Goldfeld, Z., Mroueh, Y., Sriperumbudur, B. Duality and Sample Complexity for the Gromov-Wasserstein Distance
  31. Xiong, Z., Ding, Q., Zhang, X. SyMOT-Flow: Learning optimal transport flow for two arbitrary distributions with maximum mean discrepancy
  32. Liu, X., Bai, Y., Tran, H., Zhu, Z., Thorpe, M., Kolouri, S. PTLP: Partial Transport $L^p$ Distances
  33. Sun, L., Richtárik, P. Improved Stein Variational Gradient Descent with Importance Weights
  34. Serrurier, M., Mamalet, F., Fel, T., Béthune, L., Boissin, T. On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
  35. Yan, K. Schwing, A., Wang, Y. Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
  36. Nguyen, K., Ho, N. Sliced Wasserstein Estimation with Control Variates