Cover Image

Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Karnik, Santhosh, Veselovska, Anna, Iwen, Mark, Krahmer, Felix
Format:	Preprint
Published:	2024
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

Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent
by: Liu, Zhiyu, et al.
Published: (2024)

Efficient Low-Tubal-Rank Tensor Estimation via Alternating Preconditioned Gradient Descent
by: Liu, Zhiyu, et al.
Published: (2025)

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

High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
by: Armacki, Aleksandar, et al.
Published: (2023)

A connection between Tempering and Entropic Mirror Descent
by: Chopin, Nicolas, et al.
Published: (2023)

On Regularization via Early Stopping for Least Squares Regression
by: Sonthalia, Rishi, et al.
Published: (2024)

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

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

Gradient Projection onto Historical Descent Directions for Communication-Efficient Federated Learning
by: Descours, Arnaud, et al.
Published: (2025)

Understanding the Implicit Regularization of Gradient Descent in Over-parameterized Models
by: Ma, Jianhao, et al.
Published: (2025)

Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
by: Laus, Hannah, et al.
Published: (2025)

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

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

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

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

Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems
by: Zhong, Yanjie, et al.
Published: (2024)

Function Gradient Approximation with Random Shallow ReLU Networks with Control Applications
by: Lamperski, Andrew, et al.
Published: (2024)

Optimal Regularization for a Data Source
by: Leong, Oscar, et al.
Published: (2022)

Tropical Gradient Descent
by: Talbut, Roan, et al.
Published: (2024)

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

The Star Geometry of Critic-Based Regularizer Learning
by: Leong, Oscar, et al.
Published: (2024)

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
by: Jiang, Yiheng, et al.
Published: (2023)

Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
by: Roy, Somjit, et al.
Published: (2026)

Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing
by: Lou, Mengqi, et al.
Published: (2024)

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

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

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

Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
by: Li, Yingying, et al.
Published: (2023)

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
by: Vukovic, Manojlo, et al.
Published: (2026)

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
by: Leong, Oscar, et al.
Published: (2025)

Type-II Saddles and Probabilistic Stability of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2023)

Piecewise Linearity of Min-Norm Solution Map of a Nonconvexly Regularized Convex Sparse Model
by: Zhang, Yi, et al.
Published: (2023)

An Oracle Gradient Regularized Newton Method for Quadratic Measurements Regression
by: Fan, Jun, et al.
Published: (2022)

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

Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
by: Cai, Yuhang, et al.
Published: (2025)

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification
by: Jung, Hyunji, et al.
Published: (2025)

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

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

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

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