AutoML 2024

19 papers

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

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

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

Automated Deep Learning for Load Forecasting Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Brégère

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

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

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

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

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

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

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

Improving Transfer Learning by Means of Ensemble Learning and Swarm Intelligence-Based Neuroevolution Adri Gómez, Monica Abella, Manuel Desco

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

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

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

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

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

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