AutoML 2025

25 papers

Auto-nnU-Net: Towards Automated Medical Image Segmentation

Jannis Becktepe, Leona Hennig, Steffen Oeltze-Jafra, Marius Lindauer

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

Jacob O Tørring, Carl Hvarfner, Luigi Nardi, Magnus Själander

AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting

Keshav Das, Julie Keisler, Margaux Brégère, Amaury Durand

Exploring One Million Machine Learning Pipelines: A Benchmarking Study

Edesio Alcobaça, Andre Carlos Ponce de Leon Ferreira De Carvalho

Ax: A Platform for Adaptive Experimentation

Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, Mia Garrard

\textttconfopt: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods

Abhash Kumar Jha, Shakiba Moradian, Arjun Krishnakumar, Martin Rapp, Frank Hutter

Revisiting Learning Rate Control

Micha Henheik, Theresa Eimer, Marius Lindauer

CAPO: Cost-Aware Prompt Optimization

Tom Zehle, Moritz Schlager, Timo Heiß, Matthias Feurer

Overtuning in Hyperparameter Optimization

Lennart Schneider, Bernd Bischl, Matthias Feurer

Transferrable Surrogates in Expressive Neural Architecture Search Spaces

Shiwen Qin, Gabriela Kadlecová, Martin Pilát, Roman Neruda, Elliot J. Crowley

Multi-layer Stack Ensembles for Time Series Forecasting

Nathanael Bosch, Oleksandr Shchur, Nick Erickson, Michael Bohlke-Schneider, Ali Caner Turkmen

Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization

Sigrid Passano Hellan, Huibin Shen, Francois-Xavier Aubet, David Salinas, Aaron Klein

AutoPDL: Automatic Prompt Optimization for LLM Agents

Claudio Spiess, Mandana Vaziri, Louis Mandel, Martin Hirzel

SmartCal: A Novel Automated Approach to Classifier Probability Calibration

Mohamed Maher Abdelrahman, Osama Fayez Oun, Youssef Medhat, Mariam Magdy Elseedawy, Yara Mostafa Marei

Hyperparameter Optimization via Interacting with Probabilistic Circuits

Jonas Seng, Fabrizio Ventola, Zhongjie Yu, Kristian Kersting

EG-ENAS: Efficient and Generalizable Evolutionary Neural Architecture Search for Image Classification

Mateo Avila Pava, René Groh, Andreas M Kist

What Makes Freezing Layers in Deep Neural Networks Effective? A Linear Separability Perspective

Collin Coil, Nick Cheney

Regularized Neural Ensemblers

Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter

The Ranking Trick: A Simple and Robust Alternative to Score-Based Regression for AutoML

Hernan Ceferino Vazquez, Jorge Sánchez, Verónica Bogado, Pucci Romero Tobias

syftr: Pareto-Optimal Generative AI

Alexander Conway, Debadeepta Dey, Stefan Hackmann, Matthew Hausknecht, Michael Douglas Schmidt

Fast Bayesian Optimization of Function Networks with Partial Evaluations

Poompol Buathong, Peter I. Frazier

Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization

Timur Carstensen, Neeratyoy Mallik, Frank Hutter, Martin Rapp

Iterative Monte Carlo Tree Search for Neural Architecture Search

Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli

Feasibility-Driven Trust Region Bayesian Optimization

Paolo Ascia, Elena Raponi, Thomas Bäck, Fabian Duddeck

PiML: Automated Machine Learning Workflow Optimization using LLM Agents

Abhishek Chopde, Fardeen Pettiwala, Sankar Kirubananth, Sai Kiran Botla, Pachipulusu Ayyappa Kethan