Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Python library to easily log experiments and parallelize hyperparameter search for neural networks

PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* variants (including evolutionary algorithms, swarm-based randomized optimizers, pattern search, and even random search). [https://jmlr.org/papers/v25/23-0386.html (CCF-A)] (Its Planned Extensions: PyCoPop7, PyNoPop7, PyPop77, and PyMePop7)

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

Python library for Bayesian hyper-parameters optimization

Heuristic Optimization for Python

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Tuning the Parameters of Heuristic Optimizers (Meta-Optimization / Hyper-Parameter Optimization)

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Spark Parameter Optimization and Tuning

Mixed Adaptive Random Search (MARS) for Optimization

Feature selection package of the mlr3 ecosystem.

Different hyperparameter optimization methods to get best performance for your Machine Learning Models

Hyperparameters-Optimization

Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets

Implementation of Grid Search to find better hyper-parameters for decision tree to reduce the over fitting.

Ithaka board game is played on a four by four square grid with three pieces in each of four colors.

A simple JAX-based implementation of random search for locomotion tasks using MuJoCo XLA (MJX).

Archive of my older research papers on optimization