📰 Must-read papers on Offline Model-based Optimization 🔥
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Updated
May 25, 2024
📰 Must-read papers on Offline Model-based Optimization 🔥
Powell's Derivative-Free Optimization solvers.
The SOLAR blackbox optimization problem
DOTS-Benchmark is a suite to evaluate algorithms for complex system optimisation.
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell.
A hyperparameter optimization framework, inspired by Optuna.
Generalized and Efficient Blackbox Optimization System
NOMAD - A blackbox optimization software
BOSS (Bayesian Optimization with Semiparametric Surrogate)
Python library for parallel multiobjective simulation optimization
OMADS - A blackbox optimization python package
Fast Bayesian optimization, quadrature, inference over arbitrary domain with GPU parallel acceleration
GoES: Evolution strategy for black box optimization in pure Go.
Benchmarking optimization solvers.
A Helper Library to run asynchronous optimization with Optuna
Website with interactive client-side CMA-ES (blackbox optimizer) demos. Reinforcement-learning demos allow users to control RL-trained robots.
[ICLR'24] "DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training" by Aochuan Chen*, Yimeng Zhang*, Jinghan Jia, James Diffenderfer, Jiancheng Liu, Konstantinos Parasyris, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu
Parallel Hyperparameter Tuning in Python
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