:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kunkel, Lea, Trabs, Mathias
Format:	Preprint
Published:	2024
Subjects:	Statistics Theory Machine Learning 62E17, 62G05, 68T07
Online Access:	https://arxiv.org/abs/2403.15312
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Distribution estimation via Flow Matching with Lipschitz guarantees
by: Kunkel, Lea
Published: (2025)

A PAC-Bayes oracle inequality for sparse neural networks
by: Steffen, Maximilian F., et al.
Published: (2022)

A minimum Wasserstein distance approach to Fisher's combination of independent discrete p-values
by: Contador, Gonzalo, et al.
Published: (2023)

Wasserstein Distributionally Robust Nonparametric Regression
by: Liu, Changyu, et al.
Published: (2025)

Model-free filtering in high dimensions via projection and score-based diffusions
by: Christensen, Sören, et al.
Published: (2025)

Two approaches to multiple canonical correlation analysis for repeated measures data
by: Górecki, Tomasz, et al.
Published: (2025)

Optimal Rate of Kernel Regression in Large Dimensions
by: Lu, Weihao, et al.
Published: (2023)

Mathematical Theory of Collinearity Effects on Machine Learning Variable Importance Measures
by: Bladen, Kelvyn K., et al.
Published: (2025)

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

Asymptotic confidence bands for centered purely random forests
by: Neumeyer, Natalie, et al.
Published: (2025)

Normal approximations for the multivariate inverse Gaussian distribution and asymmetric kernel smoothing on $d$-dimensional half-spaces
by: Belzile, Léo R., et al.
Published: (2022)

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

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

Distributed Sparse Linear Regression under Communication Constraints
by: Fonseca, Rodney, et al.
Published: (2023)

Error Bounds for Learning with Vector-Valued Random Features
by: Lanthaler, Samuel, et al.
Published: (2023)

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

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

Deconvolution of distribution functions without integral transforms
by: Kaiser, Henrik
Published: (2025)

Kernel $ε$-Greedy for Multi-Armed Bandits with Covariates
by: Arya, Sakshi, et al.
Published: (2023)

Spatio-temporal probabilistic forecast using MMAF-guided learning
by: Bardi, Leonardo, et al.
Published: (2026)

Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms
by: Wang, Xiaoyu, et al.
Published: (2025)

Convolutional Attention in Betting Exchange Markets
by: Gonçalves, Rui, et al.
Published: (2025)

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

Deconvolution of Arbitrary Distribution Functions and Densities
by: Kaiser, Henrik
Published: (2025)

Dirichlet kernel density estimation on the simplex with missing data
by: Daayeb, Hanen, et al.
Published: (2026)

An operator learning perspective on parameter-to-observable maps
by: Huang, Daniel Zhengyu, et al.
Published: (2024)

Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning
by: Kratsios, Anastasis, et al.
Published: (2026)

Convergence rates of non-stationary and deep Gaussian process regression
by: Osborne, Conor, et al.
Published: (2023)

Wishart kernel density estimation for strongly mixing time series on the cone of positive definite matrices
by: Belzile, Léo R., et al.
Published: (2025)

Adaptive Density Estimation Using Projection Kernels and Penalized Comparison to Overfitting
by: Hoang, Van Ha, et al.
Published: (2025)

The Optimality of Kernel Classifiers in Sobolev Space
by: Lai, Jianfa, et al.
Published: (2024)

On the Benefits of Accelerated Optimization in Robust and Private Estimation
by: Marchis, Laurentiu Andrei, et al.
Published: (2025)

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

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

Gaussian Interpolation Flows
by: Gao, Yuan, et al.
Published: (2023)

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

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

Fair Sufficient Representation Learning
by: Zhou, Xueyu, et al.
Published: (2025)

Confidence Sets for Multidimensional Scaling
by: Vishwanath, Siddharth, et al.
Published: (2025)