Cover Image

Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ye, Zeqi, Chen, Minshuo
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
by: Chen, Minshuo, et al.
Published: (2024)

Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory
by: Fu, Hengyu, et al.
Published: (2024)

Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data
by: Fu, Hengyu, et al.
Published: (2024)

Statistical Inference in Tensor Completion: Optimal Uncertainty Quantification and Statistical-to-Computational Gaps
by: Ma, Wanteng, et al.
Published: (2024)

Fine-Grained Uncertainty Quantification via Collisions
by: Friedbaum, Jesse, et al.
Published: (2024)

Neural Conditional Probability for Uncertainty Quantification
by: Kostic, Vladimir R., et al.
Published: (2024)

Uncertainty Quantification of MLE for Entity Ranking with Covariates
by: Fan, Jianqing, et al.
Published: (2022)

Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA
by: Kumar, Syamantak, et al.
Published: (2025)

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
by: Lugosi, Gábor, et al.
Published: (2025)

PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
by: Agarwal, Abhineet, et al.
Published: (2025)

Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes
by: Bergna, Richard, et al.
Published: (2025)

Training-Free Adaptation of Diffusion Models via Doob's $h$-Transform
by: Zhu, Qijie, et al.
Published: (2026)

Statistical Convergence of Spherical First Hitting Diffusion Models
by: Bienewald, Simon, et al.
Published: (2026)

High-dimensional logistic regression with missing data: Imputation, regularization, and universality
by: Verchand, Kabir Aladin, et al.
Published: (2024)

Provable Separations between Memorization and Generalization in Diffusion Models
by: Ye, Zeqi, et al.
Published: (2025)

Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
by: Hauth, Jeremiah, et al.
Published: (2024)

Statistical and Computational Efficiency for Smooth Tensor Estimation with Unknown Permutations
by: Lee, Chanwoo, et al.
Published: (2021)

Provable Efficiency of Guidance in Diffusion Models for General Data Distribution
by: Li, Gen, et al.
Published: (2025)

$L^2$ over Wasserstein: Statistical Analysis for Optimal Transport
by: Passeggeri, Riccardo, et al.
Published: (2026)

Beyond Basic A/B testing: Improving Statistical Efficiency for Business Growth
by: Wei, Changshuai, et al.
Published: (2025)

kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions
by: Pashmchi, Parastoo, et al.
Published: (2025)

Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions
by: Wu, Jingda, et al.
Published: (2026)

Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure
by: Chen, Minshuo, et al.
Published: (2025)

A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics
by: Lin, Licong, et al.
Published: (2025)

Statistical optimal transport
by: Chewi, Sinho, et al.
Published: (2024)

Statistical Inference under Performativity
by: Li, Xiang, et al.
Published: (2025)

Statistically guided deep learning
by: Kohler, Michael, et al.
Published: (2025)

Statistical and computational challenges in ranking
by: Carpentier, Alexandra, et al.
Published: (2025)

Uncertainty Quantification Via the Posterior Predictive Variance
by: Chaudhuri, Sanjay, et al.
Published: (2026)

On Uncertainty Calibration for Equivariant Functions
by: Berman, Edward, et al.
Published: (2025)

Uncertainty quantification in metric spaces
by: Lugosi, Gábor, et al.
Published: (2024)

On Quantification of Borrowing of Information in Hierarchical Bayesian Models
by: Ghosh, Prasenjit, et al.
Published: (2025)

Statistics of Min-max Normalized Eigenvalues in Random Matrices
by: Nakada, Hyakka, et al.
Published: (2025)

On the Efficiency of ERM in Feature Learning
by: Hanchi, Ayoub El, et al.
Published: (2024)

Statistical inference for pairwise comparison models
by: Han, Ruijian, et al.
Published: (2024)

Statistical Inverse Problems in Hilbert Scales
by: Rastogi, Abhishake
Published: (2022)

Statistical Learning Theory for Distributional Classification
by: Fiedler, Christian
Published: (2026)

Observable Geometry of Singular Statistical Models
by: Plummer, Sean
Published: (2026)

Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting
by: Lebedev, Andreas, et al.
Published: (2025)

Minimax Statistical Estimation under Wasserstein Contamination
by: Chao, Patrick, et al.
Published: (2023)