Distributions

Follow this topic Ask about this topic

Momentum

14 papers in the last four weeks, down 18% on the four weeks before. 0.2% of all new papers.

Jul 6Week of Sep 21

Latest papers 197

All topics

Relative Wasserstein Angle and the Problem of the $W_2$ -Nearest Gaussian Distribution
Jan 29, 2026Binshuai Wang, Peng WeiWasserstein Distance Optimal Transport Approach
Department of Computer Science, George Washington University, Washington D.C., USA · MAE, George Washington University, Washington D.C., USA
Mixture of Distributions Matters: Dynamic Sparse Attention for Efficient Video Diffusion Transformers
Jan 14, 2026Yuxi Liu, Yipeng Hu, Zekun Zhang +2Video Diffusion Models Diffusion Transformers
Peking University · University of Electronic Science and Technology of China · University of Science and Technology of China
Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs
Jan 5, 2026Amirali Ebrahimzadeh, Seyyed M. SaliliCitation Hallucination Prompt Engineering
Department of Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USA · Independent Researcher, Ann Arbor, MI 48106, USA
Distribution Matching Variational AutoEncoder
Dec 8, 2025Sen Ye, Jianning Pei, Mengde Xu +4Conditional Variational Autoencoder Generative Models
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · University of Chinese Academy of Sciences · Tencent +1
CID: Measuring Feature Importance Through Counterfactual Distributions
Nov 19, 2025Eddie Conti, Álvaro Parafita, Axel BrandoFeature Importance Counterfactual Explanation
TAIES Group, HPES Lab, Barcelona Supercomputing Center (BSC)
Stein-based Optimization of Sampling Distributions in Model Predictive Path Integral Control
Nov 3, 2025Jace Aldrich, Odest Chadwicke JenkinsModel Predictive Path Integral Stochastic Optimal Control
Laboratory for Perceptive RObotics and Grounded REasoning SystemS and Robotics Department at the University of Michigan, Ann Arbor, USA
Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization
Oct 9, 2025Yuchen Zhu, Wei Guo, Jaemoo Choi +4Large Language Model Reinforcement Learning Diffusion Language Models
Georgia Institute of Technology.
Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning
Oct 6, 2025Kai Qin, Jiaqi Wu, Jianxiang He +8Large Language Model Unlearning Forgetting
1Tsinghua University · 2The Hong Kong University of Science and Technology · 3Jianghuai Advanced Technology Center +1
How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
Oct 1, 2025Waïss Azizian, Ali HasanIn-Context Learning Distributional Information
Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France · Work done during an internship at Morgan Stanley Machine Learning Research. · Machine Learning Research, Morgan Stanley, New York, USA
Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management
Sep 30, 2025Runze Zhang, Xiaowei Zhang, Mingyang ZhaoUser Simulation Large Language Model Evaluation
Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
Apr 2, 2025Xiaobei Zou, Luolin Xiong, Kexuan Zhang +2Spatio-Temporal Forecasting Distributions
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China · Faculty of Informatics, Università della Svizzera italiana, 69000 Lugano, Switzerland · Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Discrete distributions are learnable from metastable samples
Oct 17, 2024Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra +1Metastable Samples Gibbs
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions
Sep 4, 2024Peng Wang, Huijie Zhang, Zekai Zhang +3Low-Dimensional Structure Diffusion Models
University of Macau · University of Michigan · University of California, Berkeley
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
May 29, 2024Lanting Fang, Yulian Yang, Yawei Zhang +3Physics-Guided Diffusion Causal Discovery Methods
1Beijing Institue of Technology · Southeast University · Wuhan University
New methods to compute the generalized chi-square distribution
Apr 7, 2024Abhranil DasDistributions Discriminative
Center for Perceptual Systems, and Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation
Mar 2, 2023Dun Zeng, Xiangjing Hu, Shiyu Liu +3Federated Learning Clustering
University of Electronic Science and Technology of China · Peng Cheng Lab · Harbin Institute of Technology, Shenzhen +1
ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
Date pendingThomas Thebaud, Junhyeok Lee, Laureano Moro-Velazquez +2Speech Synthesis Speaker
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA · AMIAD, Palaiseau, France · Department of Signal Theory and Communications, Universidad Carlos III de Madrid (UC3M), Leganes, Spain

Jan 29, 2026, cs.LGJ/K move · Enter open · S save

Relative Wasserstein Angle and the Problem of the $W_2$ -Nearest Gaussian Distribution

Binshuai Wang, Peng Wei

Department of Computer Science, George Washington University, Washington D.C., USA · MAE, George Washington University, Washington D.C., USA

Wasserstein Distance Optimal Transport Approach Gaussian Primitives Distributions Heterogeneity Raymap-Induced Geometry

Open paper AskPDF arXiv

Understanding the distributional structure of high-dimensional datasets has become an important topic, yet direct visual characterization is difficult. In this work, we develop a geometric framework for characterizing the distributional structure of empirical datasets by quantifying their deviation from the Gaussian family under the geometry induced by optimal transport theory. Building on the cone structure of the relative translation invariant quadratic Wasserstein

(RW_2)

space, we define two geometric quantities---the \emph{relative Wasserstein angle} and the \emph{orthogonal projection distance}---and show that they are well-defined because of the flat geometry of the filling cone between distributional rays. This formulation recasts the problem of measuring deviation from the Gaussian family as an orthogonal projection problem onto the Gaussian cone and reveals that the commonly used moment-matching Gaussian is, in general, not the

W_2

-nearest Gaussian to a non-Gaussian distribution. In one dimension, we derive closed-form expressions for the proposed quantities and extend closed-form expressions to several other location--scale families, including uniform, Laplace, and logistic distributions. In higher dimensions, we develop a numerical approximation method for the proposed quantities based on empirical optimal transport and covariance-shape optimization. Our experimental results show the empirical convergence and stability of the proposed methods and reveal that the

RW_2

angle provides a robust and consistent measure of distributional non-Gaussianity. Moreover, these results provide empirical support for its potential use as an indicator of distributional heterogeneity.

Momentum

Related topics

Latest papers 197