Copertina

Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ranganath, Aditya, Singhal, Mukesh, Marcia, Roummel
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Optimization and Control Information Theory Machine Learning Numerical Analysis
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Random Subspace Cubic-Regularization Methods, with Applications to Low-Rank Functions
di: Cartis, Coralia, et al.
Pubblicazione: (2025)

Cubic regularized subspace Newton for non-convex optimization
di: Zhao, Jim, et al.
Pubblicazione: (2024)

Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
di: Shen, Weiping, et al.
Pubblicazione: (2026)

Online Covariance Matrix Estimation in Sketched Newton Methods
di: Kuang, Wei, et al.
Pubblicazione: (2025)

State-Dependent Lyapunov Method for Rank-1 Matrix Factorization
di: Moon, Jaehong
Pubblicazione: (2026)

Learning Regularization Functionals for Inverse Problems: A Comparative Study
di: Hertrich, Johannes, et al.
Pubblicazione: (2025)

Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
di: Niu, Chengmei, et al.
Pubblicazione: (2025)

A distributed semismooth Newton based augmented Lagrangian method for distributed optimization
di: Ma, Qihao, et al.
Pubblicazione: (2026)

A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
di: Mishra, Neel, et al.
Pubblicazione: (2024)

RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees
di: Laufer, Eilon Vaknin, et al.
Pubblicazione: (2025)

Higher Order Reduced Rank Regression
di: Greenberg, Leia, et al.
Pubblicazione: (2025)

The Rank-1 Completion Problem for Cubic Tensors
di: Zhou, Jinling, et al.
Pubblicazione: (2024)

RLS Framework with Segmentation of the Forgetting Profile and Low Rank Updates
di: Stotsky, Alexander
Pubblicazione: (2025)

Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms without Preprocessing
di: Goldshlager, Gil, et al.
Pubblicazione: (2025)

Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
di: Cohen, Jeremy E., et al.
Pubblicazione: (2024)

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
di: Ma, Shaocong, et al.
Pubblicazione: (2024)

The greedy side of the LASSO: New algorithms for weighted sparse recovery via loss function-based orthogonal matching pursuit
di: Mohammad-Taheri, Sina, et al.
Pubblicazione: (2023)

Acceleration Methods
di: d'Aspremont, Alexandre, et al.
Pubblicazione: (2021)

Subhomogeneous Deep Equilibrium Models
di: Sittoni, Pietro, et al.
Pubblicazione: (2024)

Faster Randomized Methods for Orthogonality Constrained Problems
di: Shustin, Boris, et al.
Pubblicazione: (2021)

Adaptive Proximal Gradient Method for Convex Optimization
di: Malitsky, Yura, et al.
Pubblicazione: (2023)

Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
di: Zhu, Linglingzhi, et al.
Pubblicazione: (2026)

Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization
di: Nazari, Parvin, et al.
Pubblicazione: (2025)

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

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

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
di: O'Leary-Roseberry, Thomas, et al.
Pubblicazione: (2024)

Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
di: Yuan, Ganzhao
Pubblicazione: (2026)

A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
di: Fang, Yuchen, et al.
Pubblicazione: (2026)

The Nondecreasing Rank
di: McCormack, Andrew
Pubblicazione: (2025)

A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints
di: Yuan, Ganzhao
Pubblicazione: (2023)

Symmetry & Critical Points for Symmetric Tensor Decomposition Problems
di: Arjevani, Yossi, et al.
Pubblicazione: (2023)

On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
di: Xia, Lu, et al.
Pubblicazione: (2023)

Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging
di: Banerjee, Amartya, et al.
Pubblicazione: (2024)

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
di: Liu, Shu, et al.
Pubblicazione: (2024)

Minimisation of Submodular Functions Using Gaussian Zeroth-Order Random Oracles
di: Farzin, Amir Ali, et al.
Pubblicazione: (2025)

Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems
di: Karris, Nicholas, et al.
Pubblicazione: (2024)

Unsupervised Ground Metric Learning
di: Auffenberg, Janis, et al.
Pubblicazione: (2025)

A thorough study of Riemannian Newton's Method
di: da Silva, Caio O., et al.
Pubblicazione: (2025)

Learning incomplete factorization preconditioners for GMRES
di: Häusner, Paul, et al.
Pubblicazione: (2024)

Machine Learning and Control: Foundations, Advances, and Perspectives
di: Zuazua, Enrique
Pubblicazione: (2025)