Cover Image

Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Coppola, Corrado, Papa, Lorenzo, Amerini, Irene, Palagi, Laura
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CMA Light: a novel Minibatch Algorithm for large-scale non convex finite sum optimization
by: Coppola, Corrado, et al.
Published: (2023)

Computational issues in Optimization for Deep networks
by: Coppola, Corrado, et al.
Published: (2024)

Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness
by: Seccia, Ruggiero, et al.
Published: (2022)

Unboxing Tree Ensembles for interpretability: a hierarchical visualization tool and a multivariate optimal re-built tree
by: Di Teodoro, Giulia, et al.
Published: (2023)

A second-order-like optimizer with adaptive gradient scaling for deep learning
by: Bolte, Jérôme, et al.
Published: (2024)

Fast sparse optimization via adaptive shrinkage
by: Cerone, Vito, et al.
Published: (2025)

A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy
by: Lyu, Jiameng, et al.
Published: (2024)

A stochastic gradient method for trilevel optimization
by: Giovannelli, Tommaso, et al.
Published: (2025)

Dealing with unbounded gradients in stochastic saddle-point optimization
by: Neu, Gergely, et al.
Published: (2024)

Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search
by: Barnhart, Cynthia, et al.
Published: (2024)

Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization
by: Le, Tam
Published: (2025)

Fast Algorithm for Constrained Linear Inverse Problems
by: Sheriff, Mohammed Rayyan, et al.
Published: (2022)

Adaptive multi-gradient methods for quasiconvex vector optimization and applications to multi-task learning
by: Minh, Nguyen Anh, et al.
Published: (2024)

Snacks: a fast large-scale kernel SVM solver
by: Tanji, Sofiane, et al.
Published: (2023)

Strong bounds for large-scale Minimum Sum-of-Squares Clustering
by: Croella, Anna Livia, et al.
Published: (2025)

Beyond Short Steps in Frank-Wolfe Algorithms
by: Martínez-Rubio, David, et al.
Published: (2025)

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

An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learning
by: Salehi, Mohammad Sadegh, et al.
Published: (2023)

Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
by: Sambharya, Rajiv, et al.
Published: (2024)

Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes
by: Lan, Guanghui, et al.
Published: (2024)

Local adapt-then-combine algorithms for distributed nonsmooth optimization: Achieving provable communication acceleration
by: Guo, Luyao, et al.
Published: (2026)

On improving generalization in a class of learning problems with the method of small parameters for weakly-controlled optimal gradient systems
by: Befekadu, Getachew K.
Published: (2024)

Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond
by: Soma, Tasuku, et al.
Published: (2022)

Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
by: Dereich, Steffen, et al.
Published: (2024)

On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization
by: Lazić, Petra, et al.
Published: (2026)

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

Tight Rates for Bandit Control Beyond Quadratics
by: Sun, Y. Jennifer, et al.
Published: (2024)

Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks
by: Jentzen, Arnulf, et al.
Published: (2024)

CaVE: A Cone-Aligned Approach for Fast Predict-then-optimize with Binary Linear Programs
by: Tang, Bo, et al.
Published: (2023)

TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
by: Li, Xiang, et al.
Published: (2022)

Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
by: Guo, Xin, et al.
Published: (2023)

Almost sure convergence rates of stochastic gradient methods under gradient domination
by: Weissmann, Simon, et al.
Published: (2024)

A theoretical and empirical study of new adaptive algorithms with additional momentum steps and shifted updates for stochastic non-convex optimization
by: Alecsa, Cristian Daniel
Published: (2021)

Control and optimization for Neural Partial Differential Equations in Supervised Learning
by: Bensoussan, Alain, et al.
Published: (2025)

Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
by: Ding, Shihong, et al.
Published: (2026)

Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm
by: Li, Yueyi, et al.
Published: (2024)

Beyond likelihood ratio bias: Nested multi-time-scale stochastic approximation for likelihood-free parameter estimation
by: Li, Zehao, et al.
Published: (2024)

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
by: Saves, Paul, et al.
Published: (2025)

Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
by: Li, Yuchen, et al.
Published: (2023)

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
by: Vaswani, Sharan, et al.
Published: (2021)