Quantized Optimal Transport Reward-based Reinforcement Learning Approach to Detoxify Query Auto-Completion - Lecture 1
Explore a novel reinforcement learning approach for detoxifying query auto-completion using quantized optimal transport rewards.
Optimal Transport Enhanced Cross-City Site Recommendation - T2.4
Explore optimal transport techniques for cross-city site recommendations, enhancing POI suggestions through advanced computational methods.
Uncovering Cellular Dynamics and Metabolism with Geometric Deep Learning and Optimal Transport
Explore geometric deep learning and optimal transport techniques for uncovering cellular dynamics and metabolism in this insightful computational genomics presentation.
Optimal Transportation, Monge-Ampère Equations and Riemannian Ricci Bounds - Lecture 3
Explore geometric measure theory and optimal transport with a focus on Monge-Ampère equations and Riemannian Ricci bounds in this advanced mathematics lecture by L. Ambrosio.
Statistical Estimation of Optimal Transport Potentials
Explorez les méthodes statistiques pour estimer les potentiels de transport optimal et leurs applications en analyse de données.
Structured Prediction with Sparse Wasserstein Barycenters
Explorez la prédiction structurée avec les barycentres de Wasserstein clairsemés. Apprenez les concepts clés et les applications de cette technique mathématique avancée.
Barycenters for Probability Distributions Based on Optimal Weak Mass Transport
Explore optimal weak mass transport for probability distributions, focusing on barycenters and their applications in mathematical analysis.
Some Bounds for the Wasserstein Distance Between the Empirical Measure
Explorez les limites de la distance de Wasserstein entre la mesure empirique, en approfondissant les concepts mathématiques avancés et leurs applications statistiques.
Data-Driven Decision Making with Entropic Risk Measure
Explore data-driven decision-making using entropic risk measure. Learn bias correction techniques and apply them to project selection, portfolio optimization, and insurance pricing.
Optimal Transport for Graphs: Definitions and Applications to Graph-Signal Processing
Explore optimal transport for graphs, including definitions and applications to graph-signal processing. Gain insights from Nicolas Courty's expert perspective.
Breaking the Curse of Dimension in Quantum Mechanical Computations Through Analysis and Probability
Explore quantum mechanical computations, focusing on breaking dimensional constraints through analysis and probability. Learn about density functional theory and recent approaches to tackle existing issues.
Neural SDEs, Deep Learning and Stochastic Control
Explore neural SDEs, deep learning, and stochastic control in finance, covering optimal transport, neural networks, calibration, and simulations with practical applications.
How Optimal Transport Can Help Us to Determine Curvature of Complex Networks
Explore Ollivier Ricci curvature in complex networks, its applications in network analysis, and implementation in chemical reaction networks using optimal transport theory.
Learning Distances for Attributed Graphs with Optimal Transport
Explore optimal transport techniques for learning distances between attributed graphs, enhancing graph analysis and comparison methods in data science.
Studying the Space of Persistence Diagrams Using Optimal Partial Transport II
Exploring persistence diagrams through optimal transport theory, bridging topological data analysis with measure spaces to characterize convergence and prove new results in random settings.
Particle Methods for Optimization Over Measures - Lecture 2
Explore particle methods for measure optimization, focusing on theoretical foundations and practical applications in data science and machine learning.
Computational Optimal Transport - A Mini Tutorial
Overview of novel approaches to compute optimal transport, blending recent techniques including convex optimization, automatic differentiation, and neural networks for large-scale approximations in high-dimensional regimes.
Topological Node2vec: Improving Graph Embeddings with Persistent Homology
Exploring persistent homology to enhance graph embeddings, combining topology with machine learning for improved data analysis and representation in complex networks.
Convergent Semi-Lagrangian Methods for the Monge-Ampère Equation on Unstructured Grids
Explores semi-Lagrangian discretisation of Monge-Ampère operator on P1 finite element spaces, discussing challenges and solutions for numerical methods in solving Monge-Ampère equations, with applications in inverse reflector problems.
Regularity Theory for Area-Minimizing Currents
Explore advanced concepts in geometric measure theory, including zero forms, currents, and rectifiability theorems, with applications to area-minimizing problems in mathematics.
Regularity Theory for Area-Minimizing Currents
Explore advanced concepts in geometric measure theory, including currents, boundaries, and regularity theory for area-minimizing surfaces, with applications to optimal transport problems.