18 citations · 30 across the 6 of their papers we have counts for
6 papers · 1 filter
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
Sebastian Pineda Arango, Fabio Ferreira, Arlind Kadra +2
With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dat…
Interpretable Mesomorphic Networks for Tabular Data
Arlind Kadra, Sebastian Pineda Arango, Josif Grabocka
Even though neural networks have been long deployed in applications involving tabular data, still existing neural architectures are not explainable by design. In this paper, we pro…
Scaling Laws for Hyperparameter Optimization
Arlind Kadra, Maciej Janowski, Martin Wistuba +1
Hyperparameter optimization is an important subfield of machine learning that focuses on tuning the hyperparameters of a chosen algorithm to achieve peak performance. Recently, the…
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Martin Wistuba, Arlind Kadra, Josif Grabocka
Multi-fidelity (gray-box) hyperparameter optimization techniques (HPO) have recently emerged as a promising direction for tuning Deep Learning methods. However, existing methods su…
Well-tuned Simple Nets Excel on Tabular Datasets
Arlind Kadra, Marius Lindauer, Frank Hutter +1
Tabular datasets are the last "unconquered castle" for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent…
OpenML-Python: an extensible Python API for OpenML
Matthias Feurer, Jan N. van Rijn, Arlind Kadra +6
OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper we introduce Open…