Image de couverture de livre

Risk reversal for least squares estimators under nested convex constraints

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Al-Ghattas, Omar
Format:	Preprint
Publié:	2026
Sujets:	Statistics Theory Machine Learning Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Learning linear dynamical systems under convex constraints
par: Tyagi, Hemant, et autres
Publié: (2023)

High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
par: Balasubramanian, Krishnakumar, et autres
Publié: (2023)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
par: Zhang, Dake, et autres
Publié: (2024)

Off-policy estimation with adaptively collected data: the power of online learning
par: Lee, Jeonghwan, et autres
Publié: (2024)

Learning an Optimal Assortment Policy under Observational Data
par: Han, Yuxuan, et autres
Publié: (2025)

The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
par: Collins-Woodfin, Elizabeth, et autres
Publié: (2024)

Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
par: Bravo, Mario, et autres
Publié: (2025)

Error Analysis of Triangular Optimal Transport Maps for Filtering
par: Al-Jarrah, Mohammad, et autres
Publié: (2025)

Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity
par: Stöger, Dominik, et autres
Publié: (2024)

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

RandALO: Out-of-sample risk estimation in no time flat
par: Nobel, Parth, et autres
Publié: (2024)

Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality
par: Caprio, Rocco, et autres
Publié: (2024)

The out-of-sample prediction error of the square-root-LASSO and related estimators
par: Olea, José Luis Montiel, et autres
Publié: (2022)

High-dimensional Limit of SGD for Diagonal Linear Networks
par: Malaxechebarría, Begoña García, et autres
Publié: (2026)

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

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
par: Vukovic, Manojlo, et autres
Publié: (2026)

Data-Efficient Non-Gaussian Semi-Nonparametric Density Estimation for Nonlinear Dynamical Systems
par: Liao, Aaron R., et autres
Publié: (2026)

Computation of Least Trimmed Squares: A Branch-and-Bound framework with Hyperplane Arrangement Enhancements
par: Meng, Xiang, et autres
Publié: (2026)

Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
par: Roy, Somjit, et autres
Publié: (2026)

Trajectory-Restricted Optimization Conditions and Geometry-Aware Linear Convergence
par: Chaudhry, Faris, et autres
Publié: (2026)

Continuous-time reinforcement learning: ellipticity enables model-free value function approximation
par: Mou, Wenlong
Publié: (2026)

Robust Assortment Optimization from Observational Data
par: Lu, Miao, et autres
Publié: (2026)

Stochastic Optimization with Optimal Importance Sampling
par: Aolaritei, Liviu, et autres
Publié: (2025)

A Theory of Feature Learning in Kernel Models
par: Chen, Yunlu, et autres
Publié: (2023)

Joint learning of a network of linear dynamical systems via total variation penalization
par: Donnat, Claire, et autres
Publié: (2025)

A review of NMF, PLSA, LBA, EMA, and LCA with a focus on the identifiability issue
par: Qi, Qianqian, et autres
Publié: (2025)

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
par: Gao, Yihang, et autres
Publié: (2025)

Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity
par: Liang, Tengyuan, et autres
Publié: (2024)

Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
par: Bennouna, Amine, et autres
Publié: (2021)

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
par: Liang, Tengyuan
Publié: (2022)

Extreme mass distributions for quasi-copulas
par: Omladič, Matjaž, et autres
Publié: (2025)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
par: Cheng, Xiuyuan, et autres
Publié: (2023)

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
par: Jiang, Yiheng, et autres
Publié: (2023)

An Elementary Proof of the Near Optimality of LogSumExp Smoothing
par: Samakhoana, Thabo, et autres
Publié: (2025)

Iteratively reweighted kernel machines efficiently learn sparse functions
par: Zhu, Libin, et autres
Publié: (2025)

Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
par: Zhang, Zhiheng, et autres
Publié: (2024)

Federated Optimization of Smooth Loss Functions
par: Jadbabaie, Ali, et autres
Publié: (2022)

On the Uniform Convergence of Subdifferentials in Stochastic Optimization and Learning
par: Ruan, Feng
Publié: (2024)

A New Perspective On Denoising Based On Optimal Transport
par: Trillos, Nicolas Garcia, et autres
Publié: (2023)

Decentralized Sparse Linear Regression via Gradient-Tracking: Linear Convergence and Statistical Guarantees
par: Maros, Marie, et autres
Publié: (2022)