Imagen de Portada

Quasi-Bayes meets Vines

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Huk, David, Zhang, Yuanhe, Steel, Mark, Dutta, Ritabrata
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning 62G07
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Your copula is a classifier in disguise: classification-based copula density estimation
por: Huk, David, et al.
Publicado: (2024)

TILT: Target-induced loss tilting under covariate shift
por: Yamamoto, Kakei, et al.
Publicado: (2026)

Estimating Unbounded Density Ratios: Applications in Error Control under Covariate Shift
por: Xu, Shuntuo, et al.
Publicado: (2025)

Wasserstein Distributionally Robust Nonparametric Regression
por: Liu, Changyu, et al.
Publicado: (2025)

On the minimax optimality of Flow Matching through the connection to kernel density estimation
por: Kunkel, Lea, et al.
Publicado: (2025)

Distribution estimation via Flow Matching with Lipschitz guarantees
por: Kunkel, Lea
Publicado: (2025)

Estimation and Uniform Inference in Sparse High-Dimensional Additive Models
por: Bach, Philipp, et al.
Publicado: (2020)

Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
por: Li, Keyi, et al.
Publicado: (2025)

From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps
por: He, Chenxuan, et al.
Publicado: (2025)

Generalization Error of GAN from the Discriminator's Perspective
por: Yang, Hongkang, et al.
Publicado: (2021)

Transcendental Regularization of Finite Mixtures:Theoretical Guarantees and Practical Limitations
por: Fokoué, Ernest
Publicado: (2026)

Predictive Hotspot Mapping for Data-driven Crime Prediction
por: Sriram, Karthik, et al.
Publicado: (2026)

Convergence of Continuous Normalizing Flows for Learning Probability Distributions
por: Gao, Yuan, et al.
Publicado: (2024)

Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models
por: Huang, Ding, et al.
Publicado: (2024)

Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models
por: Austern, Morgane, et al.
Publicado: (2025)

Gaussian Interpolation Flows
por: Gao, Yuan, et al.
Publicado: (2023)

Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data
por: Ge, Yeheng, et al.
Publicado: (2025)

Conditional Stochastic Interpolation for Generative Learning
por: Huang, Ding, et al.
Publicado: (2023)

Fair Sufficient Representation Learning
por: Zhou, Xueyu, et al.
Publicado: (2025)

Support-Conditioned Flow Matching Is Kernel Smoothing
por: Smola, Daniel Matsui
Publicado: (2026)

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation
por: Zabolotnii, Serhii
Publicado: (2026)

Bayes with No Shame: Admissibility Geometries of Predictive Inference
por: Polson, Nicholas G., et al.
Publicado: (2026)

Non-asymptotic analysis of the performance of the penalized least trimmed squares in sparse models
por: Zuo, Yijun
Publicado: (2025)

Nonparametric estimation of a factorizable density using diffusion models
por: Kwon, Hyeok Kyu, et al.
Publicado: (2025)

Regularized least squares learning with heavy-tailed noise is minimax optimal
por: Mollenhauer, Mattes, et al.
Publicado: (2025)

Generalized Naive Bayes
por: Kovács, Edith Alice, et al.
Publicado: (2024)

Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
por: Wang, Kaizheng
Publicado: (2023)

Diagonal Linear Networks and the Lasso Regularization Path
por: Berthier, Raphaël
Publicado: (2025)

The Strong Screening Rule for SLOPE
por: Larsson, Johan, et al.
Publicado: (2020)

The Hessian Screening Rule
por: Larsson, Johan, et al.
Publicado: (2021)

Look-Ahead Screening Rules for the Lasso
por: Larsson, Johan
Publicado: (2021)

The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility
por: Uehara, Eichi
Publicado: (2025)

Optimal empirical Bayes estimation for the Poisson model via minimum-distance methods
por: Jana, Soham, et al.
Publicado: (2022)

A Wasserstein perspective of Vanilla GANs
por: Kunkel, Lea, et al.
Publicado: (2024)

Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
por: Anderberg, Alastair, et al.
Publicado: (2024)

Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers
por: Ching, Michelle, et al.
Publicado: (2026)

Generalized Factor Neural Network Model for High-dimensional Regression
por: Guo, Zichuan, et al.
Publicado: (2025)

Structural Incompatibility of Differentiable Sorting and Within-Vector Rank Normalization
por: Kim, Taeyun
Publicado: (2025)

Optimal In-context Adaptivity and Distributional Robustness of Transformers
por: Ma, Tianyi, et al.
Publicado: (2025)

Provable Adversarial Robustness in In-Context Learning
por: Zhang, Di
Publicado: (2026)