Copertina

Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhu, Linglingzhi, Lee, Jonghyeok, Xie, Yao
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Methodology Optimization and Control Statistics Theory Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
di: Zhang, Yangjing, et al.
Pubblicazione: (2022)

Estimating Higher-Order Mixed Memberships via the $\ell_{2,\infty}$ Tensor Perturbation Bound
di: Agterberg, Joshua, et al.
Pubblicazione: (2022)

Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss
di: Guo, Zijian, et al.
Pubblicazione: (2025)

Low-Rank Thinning
di: Carrell, Annabelle Michael, et al.
Pubblicazione: (2025)

$K$-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control
di: Giegrich, Michael, et al.
Pubblicazione: (2023)

Causal Invariance Learning via Efficient Nonconvex Optimization
di: Wang, Zhenyu, et al.
Pubblicazione: (2024)

Selecting the Best Optimizing System
di: Si, Nian, et al.
Pubblicazione: (2022)

Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay
di: Luo, Yuetian, et al.
Pubblicazione: (2022)

Efficient Online Learning in Interacting Particle Systems
di: Sharrock, Louis, et al.
Pubblicazione: (2026)

Gradient descent inference in empirical risk minimization
di: Han, Qiyang, et al.
Pubblicazione: (2024)

Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood
di: Aragam, Bryon, et al.
Pubblicazione: (2024)

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
di: Zhu, Linglingzhi, et al.
Pubblicazione: (2024)

Optimal estimators of cross-partial derivatives and surrogates of functions
di: Lamboni, Matieyendou
Pubblicazione: (2024)

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

On the Sample Complexity of Set Membership Estimation for Linear Systems with Disturbances Bounded by Convex Sets
di: Xu, Haonan, et al.
Pubblicazione: (2024)

A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
di: Alfano, Carlo, et al.
Pubblicazione: (2023)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
di: Cheng, Xiuyuan, et al.
Pubblicazione: (2023)

Decentralized Sparse Linear Regression via Gradient-Tracking: Linear Convergence and Statistical Guarantees
di: Maros, Marie, et al.
Pubblicazione: (2022)

A novel sequential method for building upper and lower bounds of moments of distributions
di: Martin, Solal, et al.
Pubblicazione: (2025)

Randomization Inference When N Equals One
di: Liang, Tengyuan, et al.
Pubblicazione: (2023)

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

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
di: Yang, Zhuoran, et al.
Pubblicazione: (2020)

High-dimensional Limit of SGD for Diagonal Linear Networks
di: Malaxechebarría, Begoña García, et al.
Pubblicazione: (2026)

Trajectory-Restricted Optimization Conditions and Geometry-Aware Linear Convergence
di: Chaudhry, Faris, et al.
Pubblicazione: (2026)

Worst-case generation via minimax optimization in Wasserstein space
di: Cheng, Xiuyuan, et al.
Pubblicazione: (2025)

Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality
di: Wibisono, Andre
Pubblicazione: (2025)

A Spectral Framework for Closed-Form Relative Density Estimation
di: Bach, Francis
Pubblicazione: (2026)

Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
di: Agrawalla, Bhavya, et al.
Pubblicazione: (2023)

Joint Learning of Linear Dynamical Systems under Smoothness Constraints
di: Tyagi, Hemant
Pubblicazione: (2024)

Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
di: Zhang, Zhiheng, et al.
Pubblicazione: (2024)

Generating Correlation Matrices with Graph Structures Using Convex Optimization
di: Fakhar, Ali, et al.
Pubblicazione: (2025)

Data-Efficient Non-Gaussian Semi-Nonparametric Density Estimation for Nonlinear Dynamical Systems
di: Liao, Aaron R., et al.
Pubblicazione: (2026)

Off-policy estimation with adaptively collected data: the power of online learning
di: Lee, Jeonghwan, et al.
Pubblicazione: (2024)

The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
di: Huo, Dongyan, et al.
Pubblicazione: (2024)

Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis
di: Paik, Seunghoon, et al.
Pubblicazione: (2025)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
di: Li, Gen, et al.
Pubblicazione: (2020)

Inference-Time Alignment for Diffusion Models via Variationally Stable Doob's Matching
di: Chang, Jinyuan, et al.
Pubblicazione: (2026)

Iteratively reweighted kernel machines efficiently learn sparse functions
di: Zhu, Libin, et al.
Pubblicazione: (2025)

Kernel-based Optimally Weighted Conformal Time-Series Prediction
di: Lee, Jonghyeok, et al.
Pubblicazione: (2024)

Distributionally Robust Instrumental Variables Estimation
di: Qu, Zhaonan, et al.
Pubblicazione: (2024)