AutoML 2025

25 papers

\texttt{confopt}: A Library for Implementation and Evaluation of Gradient-Based One-Shot NAS Methods Abhash Kumar Jha, Shakiba Moradian, Arjun Krishnakumar, Martin Rapp, Frank Hutter

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Auto-nnU-Net: Towards Automated Medical Image Segmentation Jannis Becktepe, Leona Hennig, Steffen Oeltze-Jafra, Marius Lindauer

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AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting Keshav Das, Julie Keisler, Margaux Brégère, Amaury Durand

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AutoPDL: Automatic Prompt Optimization for LLM Agents Claudio Spiess, Mandana Vaziri, Louis Mandel, Martin Hirzel

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Ax: A Platform for Adaptive Experimentation Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, Mia Garrard, Samuel Daulton, Zhiyuan Jerry Lin, Sebastian Ament, Bernard Beckerman, Eric Onofrey, Paschal Igusti, Cristian Lara, Benjamin Letham, Cesar Cardoso, Shiyun Sunny Shen, Andy Chenyuan Lin, Matthew Grange, Elena Kashtelyan, David Eriksson, Maximilian Balandat, Eytan Bakshy

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CAPO: Cost-Aware Prompt Optimization Tom Zehle, Moritz Schlager, Timo Heiß, Matthias Feurer

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CATBench: A Compiler Autotuning Benchmarking Suite for Black-Box Optimization Jacob O Tørring, Carl Hvarfner, Luigi Nardi, Magnus Själander

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EG-ENAS: Efficient and Generalizable Evolutionary Neural Architecture Search for Image Classification Mateo Avila Pava, René Groh, Andreas M Kist

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Exploring One Million Machine Learning Pipelines: A Benchmarking Study Edesio Alcobaça, Andre Carlos Ponce de Leon Ferreira De Carvalho

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Fast Bayesian Optimization of Function Networks with Partial Evaluations Poompol Buathong, Peter I. Frazier

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Feasibility-Driven Trust Region Bayesian Optimization Paolo Ascia, Elena Raponi, Thomas Bäck, Fabian Duddeck

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Frozen Layers: Memory-Efficient Many-Fidelity Hyperparameter Optimization Timur Carstensen, Neeratyoy Mallik, Frank Hutter, Martin Rapp

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Hyperparameter Optimization via Interacting with Probabilistic Circuits Jonas Seng, Fabrizio Ventola, Zhongjie Yu, Kristian Kersting

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Iterative Monte Carlo Tree Search for Neural Architecture Search Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli

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Multi-Layer Stack Ensembles for Time Series Forecasting Nathanael Bosch, Oleksandr Shchur, Nick Erickson, Michael Bohlke-Schneider, Ali Caner Turkmen

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Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization Sigrid Passano Hellan, Huibin Shen, Francois-Xavier Aubet, David Salinas, Aaron Klein

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Overtuning in Hyperparameter Optimization Lennart Schneider, Bernd Bischl, Matthias Feurer

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PiML: Automated Machine Learning Workflow Optimization Using LLM Agents Abhishek Chopde, Fardeen Pettiwala, Sankar Kirubananth, Sai Kiran Botla, Pachipulusu Ayyappa Kethan

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Regularized Neural Ensemblers Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka

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Revisiting Learning Rate Control Micha Henheik, Theresa Eimer, Marius Lindauer

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SmartCal: A Novel Automated Approach to Classifier Probability Calibration Mohamed Maher Abdelrahman, Osama Fayez Oun, Youssef Medhat, Mariam Magdy Elseedawy, Yara Mostafa Marei, Abdullah Ibrahim, Radwa Mohamed El Shawi

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Syftr: Pareto-Optimal Generative AI Alexander Conway, Debadeepta Dey, Stefan Hackmann, Matthew Hausknecht, Michael Douglas Schmidt, Mark Lewis Steadman, Nick Volynets

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The Ranking Trick: A Simple and Robust Alternative to Score-Based Regression for AutoML Hernan Ceferino Vazquez, Jorge Sánchez, Verónica Bogado, Pucci Romero Tobias

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Transferrable Surrogates in Expressive Neural Architecture Search Spaces Shiwen Qin, Gabriela Kadlecová, Martin Pilát, Shay B Cohen, Roman Neruda, Elliot J. Crowley, Jovita Lukasik, Linus Ericsson

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What Makes Freezing Layers in Deep Neural Networks Effective? a Linear Separability Perspective Collin Coil, Nick Cheney

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