Copertina

Efficient algorithms for regularized Poisson Non-negative Matrix Factorization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Perraudin, Nathanaël, Teutrie, Adrien, Hébert, Cécile, Obozinski, Guillaume
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Sum-of-norms regularized Nonnegative Matrix Factorization
di: Ang, Andersen, et al.
Pubblicazione: (2024)

Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method
di: Yang, Yang, et al.
Pubblicazione: (2025)

Sharpness of Minima in Deep Matrix Factorization
di: Kamber, Anil, et al.
Pubblicazione: (2025)

Convergence of Alternating Gradient Descent for Matrix Factorization
di: Ward, Rachel, et al.
Pubblicazione: (2023)

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
di: Orvieto, Antonio, et al.
Pubblicazione: (2024)

An Iterative Algorithm for Regularized Non-negative Matrix Factorizations
di: Pav, Steven E.
Pubblicazione: (2024)

Fast and Effective Computation of Generalized Symmetric Matrix Factorization
di: Yang, Lei, et al.
Pubblicazione: (2026)

A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization
di: Pham, Mai-Quyen, et al.
Pubblicazione: (2023)

Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
di: Zhang, Gavin, et al.
Pubblicazione: (2025)

Majorization-minimization for Sparse Nonnegative Matrix Factorization with the $β$-divergence
di: Marmin, Arthur, et al.
Pubblicazione: (2022)

An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems
di: Zhang, Huiling, et al.
Pubblicazione: (2023)

Global Convergence of Four-Layer Matrix Factorization under Random Initialization
di: Luo, Minrui, et al.
Pubblicazione: (2025)

Implicit Regularization in Perturbed Deep Matrix Factorization: Spectral Conditions and Stability
di: Wang, Jingzhe, et al.
Pubblicazione: (2026)

Efficient algorithms for implementing incremental proximal-point methods
di: Shtoff, Alex
Pubblicazione: (2022)

Implicit Bias in Matrix Factorization and its Explicit Realization in a New Architecture
di: Hou, Yikun, et al.
Pubblicazione: (2025)

An Efficient Alternating Algorithm for ReLU-based Symmetric Matrix Decomposition
di: Wang, Qingsong
Pubblicazione: (2025)

Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks
di: Xu, Zhenghao, et al.
Pubblicazione: (2024)

Sparse Hyperparametric Itakura-Saito Nonnegative Matrix Factorization via Bi-Level Optimization
di: Selicato, Laura, et al.
Pubblicazione: (2025)

Joint Learning in the Gaussian Single Index Model
di: Pillaud-Vivien, Loucas, et al.
Pubblicazione: (2025)

On the Crucial Role of Initialization for Matrix Factorization
di: Li, Bingcong, et al.
Pubblicazione: (2024)

Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent
di: Liu, Zhiyu, et al.
Pubblicazione: (2025)

Efficient algorithms for the Hadamard decomposition
di: Wertz, Samuel, et al.
Pubblicazione: (2025)

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
di: Vossler, Patrick, et al.
Pubblicazione: (2023)

A block-coordinate descent framework for non-convex composite optimization. Application to sparse precision matrix estimation
di: Lauga, Guillaume
Pubblicazione: (2026)

A Tight Theory of Error Feedback Algorithms in Distributed Optimization
di: Thomsen, Daniel Berg, et al.
Pubblicazione: (2026)

Improved Compression Bounds for Scenario Decision Making
di: Berger, Guillaume O.
Pubblicazione: (2025)

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

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts
di: Duchemin, Quentin, et al.
Pubblicazione: (2025)

Heaviside Low-Rank Support Matrix Machine
di: Xiu, Xianchao, et al.
Pubblicazione: (2026)

Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization
di: Dubois-Taine, Benjamin, et al.
Pubblicazione: (2022)

Avoiding strict saddle points of nonconvex regularized problems
di: Bai, Luwei, et al.
Pubblicazione: (2024)

DNNLasso: Scalable Graph Learning for Matrix-Variate Data
di: Lin, Meixia, et al.
Pubblicazione: (2024)

Optimal Projection-Free Adaptive SGD for Matrix Optimization
di: Kovalev, Dmitry
Pubblicazione: (2026)

Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
di: Akhtiamov, Danil, et al.
Pubblicazione: (2026)

Riemannian coordinate descent algorithms on matrix manifolds
di: Han, Andi, et al.
Pubblicazione: (2024)

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
di: Schaipp, Fabian, et al.
Pubblicazione: (2025)

Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift
di: Liu, Xinyu, et al.
Pubblicazione: (2026)

Annealed Sinkhorn for Optimal Transport: convergence, regularization path and debiasing
di: Chizat, Lénaïc
Pubblicazione: (2024)

Iterative regularization in classification via hinge loss diagonal descent
di: Apidopoulos, Vassilis, et al.
Pubblicazione: (2022)

Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data
di: Seraghiti, Giovanni, et al.
Pubblicazione: (2026)