Cover Image

Iteratively reweighted kernel machines efficiently learn sparse functions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhu, Libin, Davis, Damek, Drusvyatskiy, Dmitriy, Fazel, Maryam
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Optimization and Control Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
by: Zhu, Libin, et al.
Published: (2026)

When do spectral gradient updates help in deep learning?
by: Davis, Damek, et al.
Published: (2025)

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

A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity
by: Davis, Damek, et al.
Published: (2026)

Online Covariance Estimation in Nonsmooth Stochastic Approximation
by: Jiang, Liwei, et al.
Published: (2025)

The radius of statistical efficiency
by: Cutler, Joshua, et al.
Published: (2024)

Gradient descent with adaptive stepsize converges (nearly) linearly under fourth-order growth
by: Davis, Damek, et al.
Published: (2024)

What is the objective of reasoning with reinforcement learning?
by: Davis, Damek, et al.
Published: (2025)

Invariant Kernels: Rank Stabilization and Generalization Across Dimensions
by: Díaz, Mateo, et al.
Published: (2025)

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

Spectral norm bound for the product of random Fourier-Walsh matrices
by: Zhu, Libin, et al.
Published: (2025)

Stochastic optimization over proximally smooth sets
by: Davis, Damek, et al.
Published: (2020)

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

Joint learning of a network of linear dynamical systems via total variation penalization
by: Donnat, Claire, et al.
Published: (2025)

Analysing heavy-tail properties of Stochastic Gradient Descent by means of Stochastic Recurrence Equations
by: Damek, Ewa, et al.
Published: (2024)

High-probability sample complexities for policy evaluation with linear function approximation
by: Li, Gen, et al.
Published: (2023)

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models
by: Mou, Wenlong
Published: (2025)

Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
by: Chang, Serina, et al.
Published: (2024)

Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
by: Zhu, Linglingzhi, et al.
Published: (2025)

Stochastic Optimization with Optimal Importance Sampling
by: Aolaritei, Liviu, et al.
Published: (2025)

A review of NMF, PLSA, LBA, EMA, and LCA with a focus on the identifiability issue
by: Qi, Qianqian, et al.
Published: (2025)

Error Analysis of Triangular Optimal Transport Maps for Filtering
by: Al-Jarrah, Mohammad, et al.
Published: (2025)

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
by: Gao, Yihang, et al.
Published: (2025)

Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
by: Bravo, Mario, et al.
Published: (2025)

Extreme mass distributions for quasi-copulas
by: Omladič, Matjaž, et al.
Published: (2025)

Learning an Optimal Assortment Policy under Observational Data
by: Han, Yuxuan, et al.
Published: (2025)

An Elementary Proof of the Near Optimality of LogSumExp Smoothing
by: Samakhoana, Thabo, et al.
Published: (2025)

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
by: Lin, Meixia, et al.
Published: (2025)

State evolution beyond first-order methods I: Rigorous predictions and finite-sample guarantees
by: Celentano, Michael, et al.
Published: (2025)

Stopping Rules for Stochastic Gradient Descent via Anytime-Valid Confidence Sequences
by: Aolaritei, Liviu, et al.
Published: (2025)

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

Gradient Equilibrium in Online Learning: Theory and Applications
by: Angelopoulos, Anastasios N., et al.
Published: (2025)

Failure of uniform laws of large numbers for subdifferentials and beyond
by: Tian, Lai, et al.
Published: (2025)

A Theory of Feature Learning in Kernel Models
by: Chen, Yunlu, et al.
Published: (2023)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
by: Zhang, Dake, et al.
Published: (2024)

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

Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity
by: Liang, Tengyuan, et al.
Published: (2024)

Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
by: Bennouna, Amine, et al.
Published: (2021)

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

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