:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Chen, Abel C. H.
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2212.12279
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Gradient Descent Algorithm Survey
by: Fucheng, Deng, et al.
Published: (2025)

Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimization
by: Nguyen, Dai Hai, et al.
Published: (2025)

Sequential Policy Gradient for Adaptive Hyperparameter Optimization
by: Li, Zheng, et al.
Published: (2025)

From Logistic Regression to the Perceptron Algorithm: Exploring Gradient Descent with Large Step Sizes
by: Tyurin, Alexander
Published: (2024)

Accelerating Feedback-based Algorithms for Quantum Optimization Using Gradient Descent
by: Mozakka, Masih, et al.
Published: (2026)

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
by: Lin, Tianyi, et al.
Published: (2024)

Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features
by: Dunbar, Oliver R. A., et al.
Published: (2024)

Stochastic Gradient Descent with Momentum is Algorithmically Stable
by: Lei, Yunwen, et al.
Published: (2026)

How to Prove the Optimized Values of Hyperparameters for Particle Swarm Optimization?
by: Chen, Abel C. H.
Published: (2023)

HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization
by: Wever, Marcel, et al.
Published: (2025)

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

On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
by: Li, Bingrui, et al.
Published: (2024)

PMGDA: A Preference-based Multiple Gradient Descent Algorithm
by: Zhang, Xiaoyuan, et al.
Published: (2024)

Quantum Shadow Gradient Descent for Variational Quantum Algorithms
by: Heidari, Mohsen, et al.
Published: (2023)

Overtuning in Hyperparameter Optimization
by: Schneider, Lennart, et al.
Published: (2025)

Enhancing Fractional Gradient Descent with Learned Optimizers
by: Sobotka, Jan, et al.
Published: (2025)

Preconditioning for Accelerated Gradient Descent Optimization and Regularization
by: Ye, Qiang
Published: (2024)

A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent
by: Wang, Shuche, et al.
Published: (2024)

Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm
by: Zhao, Jinwei, et al.
Published: (2024)

An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization
by: Engquist, Björn, et al.
Published: (2022)

Occam Gradient Descent
by: Kausik, B. N.
Published: (2024)

Quantum Algorithm for Sparse Online Learning with Truncated Gradient Descent
by: Lim, Debbie, et al.
Published: (2024)

Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
by: Ferreira, Fabio, et al.
Published: (2026)

Super Gradient Descent: Global Optimization requires Global Gradient
by: Achour, Seifeddine
Published: (2024)

Hyperparameter Optimization in Machine Learning
by: Franceschi, Luca, et al.
Published: (2024)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

Revisiting the Initial Steps in Adaptive Gradient Descent Optimization
by: Abuduweili, Abulikemu, et al.
Published: (2024)

Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization
by: Zheng, Tianqi, et al.
Published: (2024)

Stochastic Gradient Descent for Nonparametric Additive Regression
by: Chen, Xin, et al.
Published: (2024)

Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)

Bayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space
by: Ishikawa, Kazuki, et al.
Published: (2025)

Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
by: Fehring, Lukas, et al.
Published: (2025)

Stacking as Accelerated Gradient Descent
by: Agarwal, Naman, et al.
Published: (2024)

Gradient Descent, Stochastic Optimization, and Other Tales
by: Lu, Jun
Published: (2022)

An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis
by: Li, Jinli, et al.
Published: (2024)

Cross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks
by: Li, Kevin, et al.
Published: (2024)

Corner Gradient Descent
by: Yarotsky, Dmitry
Published: (2025)

The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
by: Daskalakis, Constantinos, et al.
Published: (2018)

Anytime Acceleration of Gradient Descent
by: Zhang, Zihan, et al.
Published: (2024)

Quantum Optimization via Gradient-Based Hamiltonian Descent
by: Leng, Jiaqi, et al.
Published: (2025)