:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wang, Yuanyuan, Geng, Xi, Huang, Wei, Huang, Biwei, Gong, Mingming
Format:	Preprint
Published:	2023
Subjects:	Statistics Theory Machine Learning
Online Access:	https://arxiv.org/abs/2310.19491
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Identifiability Analysis of Linear ODE Systems with Hidden Confounders
by: Wang, Yuanyuan, et al.
Published: (2024)

Generalized Bayesian Additive Regression Trees: Theory and Software
by: Saha, Enakshi
Published: (2023)

The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
by: Graf, Maximilian, et al.
Published: (2026)

Gradient-flow SDEs have unique transient population dynamics
by: Guan, Vincent, et al.
Published: (2025)

Long-Context Linear System Identification
by: Yüksel, Oğuz Kaan, et al.
Published: (2024)

Multiple Testing of Linear Forms for Noisy Matrix Completion
by: Ma, Wanteng, et al.
Published: (2023)

Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variables
by: Sturma, Nils, et al.
Published: (2025)

A Conditional Distribution Equality Testing Framework using Deep Generative Learning
by: Zheng, Siming, et al.
Published: (2025)

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations
by: Wang, Yuanyuan, et al.
Published: (2022)

Online Quantile Regression for Nonparametric Additive Models
by: Zhan, Haoran
Published: (2026)

Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise
by: Ding, Xiucai, et al.
Published: (2026)

A Generative Approach to Quasi-Random Sampling from Copulas via Space-Filling Designs
by: Wang, Sumin, et al.
Published: (2024)

Finite-Sample Identification of Linear Regression Models with Residual-Permuted Sums
by: Szentpéteri, Szabolcs, et al.
Published: (2024)

Towards Identifiable Latent Additive Noise Models
by: Liu, Yuhang, et al.
Published: (2024)

Confidence Sequences for Generalized Linear Models via Regret Analysis
by: Clerico, Eugenio, et al.
Published: (2025)

Higher-Order Causal Structure Learning with Additive Models
by: Enouen, James, et al.
Published: (2025)

Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
by: Whitehouse, Justin, et al.
Published: (2026)

Boosting Causal Additive Models
by: Kertel, Maximilian, et al.
Published: (2024)

Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models
by: Zhang, Yihan, et al.
Published: (2022)

Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
by: Dmitriev, Daniil, et al.
Published: (2026)

Towards a mathematical theory for consistency training in diffusion models
by: Li, Gen, et al.
Published: (2024)

BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models
by: Brown, Benjamin, et al.
Published: (2022)

On Linear Separation Capacity of Self-Supervised Representation Learning
by: Wang, Shulei
Published: (2023)

Nearest-Neighbor Radii under Dependent Sampling
by: Gao, Yuanyuan, et al.
Published: (2026)

Minimax Signal Detection in Sparse Additive Models
by: Kotekal, Subhodh, et al.
Published: (2023)

Iterative Reweighted Framework Based Algorithms for Sparse Linear Regression with Generalized Elastic Net Penalty
by: Ding, Yanyun, et al.
Published: (2024)

Sharp Bounds for Poly-GNNs and the Effect of Graph Noise
by: Vinas, Luciano, et al.
Published: (2024)

A Unified Information-Theoretic Framework for Meta-Learning Generalization
by: Wen, Wen, et al.
Published: (2025)

Generalized Grade-of-Membership Estimation for High-dimensional Locally Dependent Data
by: Chen, Ling, et al.
Published: (2024)

Likelihood Matching for Diffusion Models
by: Qian, Lei, et al.
Published: (2025)

On Linear Separability under Linear Compression with Applications to Hard Support Vector Machine
by: McVay, Paul, et al.
Published: (2022)

Robustly Learning Monotone Generalized Linear Models via Data Augmentation
by: Zarifis, Nikos, et al.
Published: (2025)

Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
by: Wang, He, et al.
Published: (2024)

Transformers Meet In-Context Learning: A Universal Approximation Theory
by: Li, Gen, et al.
Published: (2025)

Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning
by: Elliott, Chris, et al.
Published: (2026)

Optimal Batched Linear Bandits
by: Ren, Xuanfei, et al.
Published: (2024)

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
by: Frei, Spencer, et al.
Published: (2022)

Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing
by: Zhang, Yihan, et al.
Published: (2023)

Block Empirical Likelihood Inference for Longitudinal Generalized Partially Linear Single-Index Models
by: Zhang, Tianni, et al.
Published: (2026)

A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
by: Ryu, J. Jon, et al.
Published: (2024)