EarlyStoppingRKHS
Algorithms optimizer
Code for analyzing early stopping in kernel boosting algorithms
Code for production of the plots in the paper "Early stopping for kernel boosting algorithms: A general analysis with localized complexities"
0 stars
2 watching
0 forks
last commit: almost 9 years agoLinked from 1 awesome list
benedekrozemberczki/awesome-gradient-boosting-papersRelated projects:
| Repository | Description | Stars |
|---|---|---|
 springdaisy/gbdt | An implementation of Gradient Boosted Decision Trees with sparse output for high-dimensional data | 0 |
 jiangoforit/yellowfin_pytorch | An optimizer that automatically tunes momentum and learning rate using local quadratic approximation. | 287 |
 younghjung/onlinemlrboostingwithvfdt | An implementation of online multi-label ranking boosting using VFDT as weak learners | 4 |
 hannah-zhou/optimization_algorithm | A comprehensive collection of optimization algorithms implemented in MATLAB | 185 |
 sjsingh91/ib-cnn | A library implementing a learning algorithm for improving classification accuracy with incremental updates and ensemble methods using neural networks | 2 |
 rentruewang/koila | A lightweight wrapper around PyTorch to prevent CUDA out-of-memory errors and optimize model execution | 1,823 |
 cluebenchmark/electra | Trains and evaluates a Chinese language model using adversarial training on a large corpus. | 140 |
 damirsvrtan/fasterer | Tools for optimizing Ruby code performance | 1,812 |
 chasedehan/boostaroota | An algorithm for fast feature selection using XGBoost and other tree-based classifiers | 219 |
 jiahuadong/fiss | Implementations of federated incremental semantic segmentation in PyTorch. | 34 |
 shengroup/fmab | Federated Multi-armed Bandits algorithm implementation for simulating cognitive radio systems and recommender systems | 9 |
 moskomule/eve.pytorch | An implementation of an optimization algorithm inspired by a 2016 research paper | 33 |
 debcaldarola/fedsam | Improving generalization in federated learning by seeking flat minima through optimization techniques | 82 |
 xidongwu/federated-minimax-and-conditional-stochastic-optimization | This project presents optimization techniques for federated learning and minimax games in the context of machine learning | 1 |
 kadenzipfel/bytepeep | An optimizer tool for low-level smart contract bytecode | 119 |