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Hyperparameter Optimization

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Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.

Source: Data-driven model for fracturing design optimization: focus on building digital database and production forecast

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/automl/DEHB

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DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization

https://github.com/automl/HPOBench

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HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO

https://github.com/automl/HPO_for_RL

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On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

https://github.com/automl/HpBandSter

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BOHB: Robust and Efficient Hyperparameter Optimization at Scale

https://github.com/automl/RoBO

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Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

https://github.com/automl/SEARL

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Sample-Efficient Automated Deep Reinforcement Learning

https://github.com/automl/SMAC3

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SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

https://github.com/automl/arlbench

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ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning

https://github.com/automl/autorl-landscape

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AutoRL Hyperparameter Landscapes

https://github.com/automl/fanova

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Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization

https://github.com/automl/fob

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Fast Optimizer Benchmark

https://github.com/automl/hpi_for_mo_automl

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Hyperparameter Importance Analysis for Multi-Objective AutoML

https://github.com/automl/ida23-mindthegap

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Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

https://github.com/automl/interactive-mo-ml

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Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning

https://github.com/automl/lmem-significance

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LMEMs for post-hoc analysis of HPO Benchmarking

https://github.com/automl/mf-prior-exp

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PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning

https://github.com/automl/multi-obj-baselines

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Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization

https://github.com/automl/nas_benchmarks

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Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization

https://github.com/automl/pfns4bo

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PFNs4BO: In-Context Learning for Bayesian Optimization

https://github.com/automl/safs

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Learning Activation Functions for Sparse Neural Networks

https://github.com/automl/transfer-hpo-framework

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Practical Transfer Learning for Bayesian Optimization

https://github.com/awesomelemon/pbt-nas

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Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search

https://github.com/awslabs/adatune

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MARTHE: Scheduling the Learning Rate Via Online Hypergradients

https://github.com/awslabs/syne-tune

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Model-based Asynchronous Hyperparameter and Neural Architecture Search

https://github.com/azoz01/liltab

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Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization

https://github.com/baofff/stability_ho

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Stability and Generalization of Bilevel Programming in Hyperparameter Optimization

https://github.com/bbopt/HyperNOMAD

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HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search

https://github.com/bingqingcheng/linear-regression-benchmarks

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BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale

https://github.com/c0pep0d/rlfl0w

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A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows

https://github.com/callowbird/Harmonica

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Hyperparameter Optimization: A Spectral Approach

https://github.com/capoe/benchml

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BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale

https://github.com/cc-hpc-itwm/HPO_LazyGPR

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Scalable Hyperparameter Optimization with Lazy Gaussian Processes

https://github.com/cc-hpc-itwm/PHS

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PHS: A Toolbox for Parallel Hyperparameter Search

https://github.com/chriswhitmire/alfalfa-yield-prediction

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Comparing Machine Learning Techniques for Alfalfa Biomass Yield Prediction

https://github.com/cjfcsjt/SILBO

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Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization

https://github.com/compstat-lmu/paper_2021_benchmarking_special_issue

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Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers

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