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#p-feurer

CAPO: Cost-Aware Prompt Optimization

AutoML 2025

#p-bischl #p-feurer #p-rueckert

Overtuning in Hyperparameter Optimization

Methods Track @AutoML 2025

#p-bischl #p-feurer

In-Context Learning of Soft Nearest Neighbor Classifiers for Intelligible Tabular Machine Learning

TRL @ACL 2025

#p-feurer

OpenML: Insights From 10 Years and More Than a Thousand Papers

Patterns 6.7. Jul. 2025

#p-bischl #p-feurer

Carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks

Preprint (Jun. 2025)

#p-feurer

Efficiently Warmstarting MCMC for BNNs

FPI @ICLR 2025

#p-bischl #p-feurer #p-ruegamer

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization

NeurIPS 2024

#p-bischl #p-feurer #p-nagler

Towards Localization via Data Embedding for TabPFN

TLR @NeurIPS 2024

#p-feurer #p-nagler

AMLTK: A Modular AutoML Toolkit in Python

The Journal of Open Source Software 9.100. Aug. 2024

#p-feurer

Position: Why We Must Rethink Empirical Research in Machine Learning

ICML 2024

#p-bischl #p-boulesteix #p-feurer #p-huellermeier #p-ruegamer

Position: A Call to Action for a Human-Centered AutoML Paradigm

ICML 2024

#p-bischl #p-feurer

Interpretable Machine Learning for TabPFN

XAI 2024

#p-bischl #p-feurer #p-nagler #p-ruegamer

Towards Quantifying the Effect of Datasets for Benchmarking: A Look at Tabular Machine Learning

DMLR @ICLR 2024

#p-bischl #p-feurer

Can Fairness Be Automated? Guidelines and Opportunities for Fairness-Aware AutoML

Journal of Artificial Intelligence Research 79. Feb. 2024

#p-bischl #p-feurer

OpenML-CTR23 - A Curated Tabular Regression Benchmarking Suite

Workshop @AutoML 2023

#p-bischl #p-feurer

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

IDA 2023

#p-bischl #p-feurer

OpenML Benchmarking Suites

Track on Datasets and Benchmarks @NeurIPS 2021

#p-bischl #p-feurer