slds-lmu/yahpo_data slds-lmu/yahpo_data

  • Stars
    star
    4
  • Rank 3,304,323 (Top 66 %)
  • Language
    R
  • License
    Other
  • Created about 3 years ago
  • Updated 3 months ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
slds-lmu/yahpo_data
github

slds-lmu

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Data required for slds-lmu/yahpo_gym

More Repositories

1

seminar_multimodal_dl

https://slds-lmu.github.io/seminar_multimodal_dl/
TeX
162
star
2

lecture_i2ml

I2ML lecture repository
HTML
147
star
3

iml_methods_limitations

Seminar on Limitations of Interpretable Machine Learning Methods
R
55
star
4

yahpo_gym

Surrogate benchmarks for HPO problems
Jupyter Notebook
26
star
5

imlplots

Create Interpretable Machine Learning plots with an interactive Shiny based dashboard
R
15
star
6

lecture_dl4nlp

Repo containing all the lecture material for the dl4nlp course
TeX
15
star
7

paper_2019_iml_measures

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition
TeX
15
star
8

lecture_iml

Jupyter Notebook
12
star
9

code_pitfalls_iml

This repository contains the code for all figures in the paper "General Pitfalls of Model-agnostic Interpretation Methods for Machine Learning Models"
R
12
star
10

i2ml

https://slds-lmu.github.io/i2ml/
Makefile
12
star
11

seminar_nlp_ss20

Link to website
TeX
11
star
12

lecture_optimization

TeX
11
star
13

tsclassification

Wrapping the Time-Series Classification Java Implementations for R
R
9
star
14

mosmafs

Multi-Objective Simultaneous Model and Feature Selection
R
8
star
15

latex-math

TeX
7
star
16

qdo_yahpo

A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models
Python
6
star
17

lecture_i2ml_learnr_tutorials

CSS
5
star
18

lrz_configs

R
5
star
19

paper_2021_categorical_feature_encodings

R
4
star
20

hpo_ela

R
4
star
21

paper_2023_survival_benchmark

Benchmark for Burk et al. (2024)
R
4
star
22

iml-shiny-summary

Shiny Dashboard showing an interpretation summary for any model
R
3
star
23

i2dl

https://slds-lmu.github.io/i2dl/
HTML
3
star
24

wildlife-ml

Python
3
star
25

paper_2021_xautoml

Jupyter Notebook
3
star
26

lecture_sl

TeX
2
star
27

vistool

R
2
star
28

wildlife-experiments

Jupyter Notebook
2
star
29

seminar_website_skeleton

TeX
2
star
30

jTSC4R

Java Time Series Classification code to use in R
Java
2
star
31

paper_2021_multi_fidelity_surrogates

Surrogate benchmarks for HPO problems
R
2
star
32

surrogates

R
2
star
33

grouped_feat_imp_and_effects

R
2
star
34

dl4nlp

https://slds-lmu.github.io/dl4nlp/
Makefile
2
star
35

rcourses_notebook_deeplearning

scetch
Jupyter Notebook
2
star
36

paper_2019_multiobjective_rfms

High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
Jupyter Notebook
2
star
37

lecture_advml

TeX
1
star
38

yahpo_exps

Experiments for yahpo gym
R
1
star
39

lecture_template

TeX
1
star
40

rcourses_notebook_clustering

Jupyter Notebook
1
star
41

paper_2019_variationalResampleDistributionShift

Variational Resampling Based Assessment of Deep Neural Networks Robustness under Distribution Shift
Python
1
star
42

mlw-htr

Jupyter Notebook
1
star
43

ame

average marginal effects for machine learning
R
1
star
44

qdo_nas

R
1
star
45

paper_2024_rpid

R
1
star
46

benchmark_2022_counterfactuals

Benchmark code for paper on counterfactuals R Package
R
1
star
47

phd_thesis_dummy_template

TeX
1
star
48

rcourses_notebook_ml

Jupyter Notebook
1
star
49

mbt_comparison

R
1
star
50

mobafeas

Model Based Feature Selection
R
1
star
51

introduction_iml_bliz_summerschool

This repository provides a short introduction into the most popular model-agnostic IML (interpretable machine learning) methods. A presentation containing the theoretical background and examples as well as excercises on real-world data are included.
HTML
1
star
52

lecture_i2dl

Introduction to Deep Leaning
Jupyter Notebook
1
star
53

lecture_service

Service repo for common infrastructure across all open source lectures
R
1
star
54

paper_2023_eagga

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models
R
1
star
55

paper_2023_regression_suite

R
1
star