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AutoML 2024

19 papers

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

Introducing HoNCAML: Holistic No-Code Auto Machine Learning

Luca Piras, Joan Albert Erráez Castelltort, Jordi Casals Grifell, Xavier de Juan Pulido, Cirus Iniesta

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

Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks

Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter

Training and Cross-Validating Machine Learning Pipelines with Limited Memory

Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar

Weight-Entanglement Meets Gradient-Based Neural Architecture Search

Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, 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

Jonas Seng, Fabian Kalter, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting

Don’t Waste Your Time: Early Stopping Cross-Validation

Edward Bergman, Lennart Purucker, Frank Hutter

Sequence Alignment-based Similarity Metric in Evolutionary Neural Architecture Search

Mateo Avila Pava, René Groh, Andreas M Kist

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

Adri Gómez, Monica Abella, Manuel Desco

FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search

Jordan Dotzel, Gang Wu, Andrew Li, Muhammad Umar, Yun Ni

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, Andrea Castellani, Giorgos Borboudakis, Ioannis Tsamardinos

Speeding up NAS with Adaptive Subset Selection

Vishak Prasad C, Colin White, Sibasis Nayak, Paarth Jain, Aziz Shameem

HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection

Yue Zhao, Leman Akoglu

Is Mamba Capable of In-Context Learning?

Riccardo Grazzi, Julien Niklas Siems, Simon Schrodi, Thomas Brox, Frank Hutter