Methods Track

• Sequence Alignment-based Similarity Metric in Evolutionary Neural Architecture Search
Mateo Avila Pava, René Groh, Andreas M. Kist

• FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
Jordan Dotzel, Gang Wu, Andrew Li, Muhammad Umar, Yun Ni, Mohamed S. Abdelfattah, Zhiru Zhang, Liqun Cheng, Martin G Dixon, Norman P Jouppi, Quoc V. Le, Sheng Li

Don’t Waste Your Time: Early Stopping Cross-Validation
Edward Bergman, Lennart Purucker, Frank Hutter

Training and Cross-Validating Machine Learning Pipelines with Limited Memory
Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar

Analyzing Few-Shot Neural Architecture Search in a Metric-Driven Framework
Timotée Ly-Manson, Mathieu Léonardon, Abdeldjalil Aissa El Bey, Ghouthi Boukli Hacene, Lukas Mauch

Confidence Interval Estimation of Predictive Performance in the Context of AutoML
Konstantinos Paraschakis, Giorgos Borboudakis, Ioannis Tsamardinos

Is Mamba Capable of In-Context Learning?
Riccardo Grazzi, Julien Niklas Siems, Simon Schrodi, Thomas Brox, Frank Hutter

ASML: A Scalable and Efficient AutoML Solution for Data Streams
Nilesh Verma, Albert Bifet, Bernhard Pfahringer, Maroua Bahri

Bi-Level One-Shot Architecture Search for Probabilistic Time Series Forecasting
Fabian Kalter, Jonas Seng, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting

Improving Transfer Learning by means of Ensemble Learning and Swarm Intelligence-based Neuroevolution
Adri Gómez

HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection
Yue Zhao, Leman Akoglu

Speeding up NAS with Adaptive Subset Selection
Vishak Prasad C, Colin White, Sibasis Nayak, Paarth Jain, Aziz Shameem, Prateek Garg, Ganesh Ramakrishnan

Weight-Entanglement Meets Gradient-Based Neural Architecture Search
Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter

ABCD Track

TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
David Salinas, Nick Erickson

HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning
Gresa Shala, Sebastian Pineda Arango, André Biedenkapp, Frank Hutter, Josif Grabocka

Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter

Introducing HoNCAML: Holistic No-Code Auto Machine Learning
Luca Piras, Joan Albert Erráez Castelltort, Jordi Casals Grifell, Xavier de Juan Pulido, Cirus Iniesta, Marina Rosell Murillo, Cristina Soler Arenys

Automated Deep Learning for load forecasting
Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Brégère

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
Zhiqiang Tang, Haoyang Fang, Su Zhou, Taojiannan Yang, Zihan Zhong, Cuixiong Hu, Katrin Kirchhoff, George Karypis