isg-siegen Goto Github PK

.github

algorithm-performance-personas

algorithmselection-eddie-

A library for doing per-instance algorithm selection

assembled

A framework to find better ensembles for (Automated) Machine Learning

auto-caserec

auto-surprise

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning :rocket:

automl_for_recommender_systems

A Comparison of AutoML, ML, AutoRecSys and RecSys algorithms on RecSys datasets to determine the potential of AutoML.

camels

camels-client

camels-server

dagstuhl-24211-checklist

fmlearn-scikit-learn-client

a modified version of scikit-learn with an additional library to make API calls for Federated Meta-Learning

fmlearn-server

Federated Meta-Learning: a concept that allows everyone to benefit from the data that is generated through machine learning libraries.

fyp-per-instance-algorithm-selection-method

lenskit-auto

An AutoRecSys Library built around LensKit. Performs automatic algorithm selection, hyperparameter optimzation and ensembling on LensKit models.

mr.-dlib-client-javascript

mr.-dlib-data-importer

mr.-dlib-proxy-server

A tool for our partners who use our JavaScript Client. With this proxy, all communication with Mr. DLib is routed through the proxy, so Mr. DLib cannot get any IPs etc from the partner's users

papercite

Bibtex plugin for wordpress

piasbenchmark

random-seed-effects

recommender-systems.com

recsys-dataloader

recsys-dataset-pruning

recsys-time-evaluation

releasing-recsys-research-code

Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)

scoring-optimizer

test-repo

This repo is for anyone in the ISG to play around with GitHub.