Equivariant Flow Matching for Deep Generative Models in Physics
Explore equivariant flow matching for efficient, simulation-free training of equivariant CNFs in physics and drug discovery, improving sampling efficiency and scalability for many-particle systems.
Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport
Explore advanced techniques for improving continuous normalizing flows, including generalized conditional flow matching and optimal transport CFM, enhancing generative modeling for various applications.
Single Cell Data Integration Using Optimal Transport - 2023
Explore single-cell data integration using optimal transport. Learn unsupervised algorithms for multi-omic studies, alignment techniques, and performance quantification in computational genomics.
The Total Variation Wasserstein Problem - A New Derivation of the Euler-Lagrange Equations
Explore a novel derivation of Euler-Lagrange equations for the Total Variation Wasserstein problem, advancing understanding in mathematical optimization and transport theory.
Error Bounds for Mean-Payoff Markov Decision Processes
Explore optimal transport techniques for error bounds in reinforcement learning, focusing on mean-payoff Markov decision processes and stochastic fixed point iterations.
Computational Optimal Transport: Mature Tools and Open Problems
Explore computational optimal transport's advancements, applications in 3D shape analysis, and open challenges. Gain insights into state-of-the-art tools for efficient distribution analysis and geometric data processing.
A Renormalisation Group Perspective on Functional Inequalities - ICBS 2024
Explore multiscale analysis for decomposing high-dimensional measures, deriving functional inequalities using renormalization group methods in statistical physics.
Entropic Optimal Transport: Convergence Rates and ODE Characterization
Explore recent advances in entropic optimal transport, focusing on convergence rates and a novel ODE-based numerical method for multi-marginal problems. Gain insights into theoretical bounds and practical applications.
A Geometric View of Optimal Transport and Generative Models - ICBS 2024
Explore geometric interpretations of optimal transport theory and its application to generative models, analyzing Sora's principles and proposing improvements for physical accuracy.
Practical Individual Fairness Algorithms in Machine Learning
Explore algorithmic fairness in machine learning, focusing on Distributional Individual Fairness. Learn to train certifiably fair models using transport-based regularization for various ML tasks.
Merging Rate of Opinions via Optimal Transport on Random Measures
Explore optimal transport on random measures to analyze opinion merging rates, focusing on Bayesian statistics applications in mathematical research.
On the Concave One-Dimensional Random Assignment Problem: Kantorovich Meets Young
Explore the concave one-dimensional random assignment problem, bridging Kantorovich and Young's theories in this mathematical conference talk by Dario Trevisan.
Path Constrained Unbalanced Optimal Transport
Explore path constrained unbalanced optimal transport with Nicolas Charon, delving into geometric statistics and shape analysis applications.
Wasserstein Gradient Flows and Applications to Sampling in Machine Learning - Lecture 2
Explore Wasserstein gradient flows and their applications to sampling in machine learning, focusing on advanced mathematical concepts and techniques.
Lorentzian Length Spaces: An Introduction to Synthetic Lorentzian Geometry
Explore synthetic Lorentzian geometry foundations, focusing on time separation functions, curvature bounds, and applications in General Relativity, including recent developments in singularity theorems.
Fixed-point Error Bounds for Mean-payoff Markov Decision Processes
Explore optimal transport techniques for deriving finite-time error bounds in reinforcement learning, focusing on mean-payoff Markov decision processes and stochastic fixed point iterations.
Non-commutative Optimal Transport - Spring 2025
Explore non-commutative optimal transport's applications in AI, quantum physics, and electronic structure theory. Delve into evolution equations, Wasserstein distances, and computational algorithms for density operators.
Optimal Transport and High Dimensional Probability - Gradient Flows on Wasserstein Space
Explore gradient flows on Wasserstein spaces, connecting optimal transport theory with high-dimensional probability concepts for advanced mathematical insights.
Optimal Transport and High-Dimensional Probability
Explore functional inequalities, convergence of flows, and logconcavity of probability measures in high-dimensional spaces through optimal transport theory.
Optimal Transport for Distributionally Robust Optimization and Applications in Machine Learning
Explore optimal transport in distributionally robust optimization, focusing on its applications in machine learning, distribution shifts, and responsible AI. Gain insights into solving challenging optimization problems.
Supervised Quantile Normalization for Matrix Factorization Using Optimal Transport
Explore advanced matrix factorization techniques using optimal transport for quantile normalization. Learn applications in dimensionality reduction and genomics from expert Marco Cuturi.