Cover Image

An Introduction to Sliced Optimal Transport

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Nguyen, Khai
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computation Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Lightspeed Geometric Dataset Distance via Sliced Optimal Transport
by: Nguyen, Khai, et al.
Published: (2025)

Streaming Sliced Optimal Transport
by: Nguyen, Khai
Published: (2025)

Summarizing Bayesian Nonparametric Mixture Posterior -- Sliced Optimal Transport Metrics for Gaussian Mixtures
by: Nguyen, Khai, et al.
Published: (2024)

Amortized Optimal Transport from Sliced Potentials
by: Truong, Minh-Phuc, et al.
Published: (2026)

Sliced-Regularized Optimal Transport
by: Nguyen, Khai
Published: (2026)

Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions
by: Nguyen, Khai, et al.
Published: (2024)

Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives
by: Balakrishnan, Sivaraman, et al.
Published: (2025)

Sliced Wasserstein with Random-Path Projecting Directions
by: Nguyen, Khai, et al.
Published: (2024)

Black Box Causal Inference: Effect Estimation via Meta Prediction
by: Bynum, Lucius E. J., et al.
Published: (2025)

Vertical Consensus Inference for High-Dimensional Random Partition
by: Nguyen, Khai, et al.
Published: (2026)

Bayesian Multiple Multivariate Density-Density Regression
by: Nguyen, Khai, et al.
Published: (2026)

Deep Optimal Experimental Design for Parameter Estimation Problems
by: Siddiqui, Md Shahriar Rahim, et al.
Published: (2024)

Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
by: Hoang, Nu, et al.
Published: (2024)

Scalable Variational Causal Discovery Unconstrained by Acyclicity
by: Hoang, Nu, et al.
Published: (2024)

Bayesian Multivariate Density-Density Regression
by: Nguyen, Khai, et al.
Published: (2025)

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
by: Nagler, Thomas, et al.
Published: (2025)

Discriminative classification with generative features: bridging Naive Bayes and logistic regression
by: Terner, Zachary, et al.
Published: (2025)

Adjustment for Confounding using Pre-Trained Representations
by: Schulte, Rickmer, et al.
Published: (2025)

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
by: Zhang, Dinghuai, et al.
Published: (2023)

Generalizing Orthogonalization for Models with Non-Linearities
by: Rügamer, David, et al.
Published: (2024)

An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
by: Liu, Qiao, et al.
Published: (2026)

Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdown
by: Okuno, Akifumi, et al.
Published: (2023)

Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification
by: Kato, Masahiro
Published: (2024)

Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling
by: Hovmand, Peter S., et al.
Published: (2025)

Text Rationalization for Robust Causal Effect Estimation
by: Zhang, Lijinghua, et al.
Published: (2025)

A Causal Lens for Evaluating Faithfulness Metrics
by: Zaman, Kerem, et al.
Published: (2025)

The Leaderboard Illusion
by: Singh, Shivalika, et al.
Published: (2025)

Majority of the Bests: Improving Best-of-N via Bootstrapping
by: Rakhsha, Amin, et al.
Published: (2025)

Partial Identification Approach to Counterfactual Fairness Assessment
by: Rho, Saeyoung, et al.
Published: (2025)

Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation
by: Bhardwaj, Dhrupad, et al.
Published: (2025)

(Mis)Fitting: A Survey of Scaling Laws
by: Li, Margaret, et al.
Published: (2025)

Identifying Causal Effects Under Functional Dependencies
by: Chen, Yizuo, et al.
Published: (2024)

Ethnography and Machine Learning: Synergies and New Directions
by: Li, Zhuofan, et al.
Published: (2024)

A Causal Analysis of CO2 Reduction Strategies in Electricity Markets Through Machine Learning-Driven Metalearners
by: Naeini, Iman Emtiazi, et al.
Published: (2024)

On the Unknowable Limits to Prediction
by: Yan, Jiani, et al.
Published: (2024)

RCT Rejection Sampling for Causal Estimation Evaluation
by: Keith, Katherine A., et al.
Published: (2023)

Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
by: Schröder, Maresa, et al.
Published: (2023)

From Ground Truth to Measurement: A Statistical Framework for Human Labeling
by: Chew, Robert, et al.
Published: (2026)

Evaluating Interventional Reasoning Capabilities of Large Language Models
by: Kasetty, Tejas, et al.
Published: (2024)

AutoEval Done Right: Using Synthetic Data for Model Evaluation
by: Boyeau, Pierre, et al.
Published: (2024)