AutoML

Keyphrases

Publications

2023

Lichuan Xiang, Rosco Hunter, Minghao Xu, Lukasz Dudziak, Hongkai Wen
Exploiting Network Compressibility and Topology in Zero-Cost NAS. AutoML (2023)
Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer
Learning Activation Functions for Sparse Neural Networks. AutoML (2023)
Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer
AutoRL Hyperparameter Landscapes. AutoML (2023)
Lennart Oswald Purucker, Joeran Beel
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure. AutoML (2023)
Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
Searching for Fairer Machine Learning Ensembles. AutoML (2023)
Roque Lopez, Raoni Lourenço, Rémi Rampin, Sonia Castelo, Aécio S. R. Santos, Jorge Henrique Piazentin Ono, Cláudio T. Silva, Juliana Freire
AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks. AutoML (2023)
José Manuel Navarro, Alexis Huet, Dario Rossi
Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection. AutoML (2023)
Iordanis Fostiropoulos, Laurent Itti
ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments. AutoML (2023)
Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Jankovic, Ana Nikolikj, Urban Skvorc, Peter Korosec, Carola Doerr, Tome Eftimov
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization. AutoML (2023)
Wuyang Chen, Wei Huang, Zhangyang Wang
"No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization. AutoML (2023)
Lennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. AutoML (2023)
Xiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, Dacheng Tao
Poisson Process for Bayesian Optimization. AutoML (2023)
Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli
Balanced Mixture of Supernets for Learning the CNN Pooling Architecture. AutoML (2023)
Marcel Aach, Eray Inanc, Rakesh Sarma, Morris Riedel, Andreas Lintermann
Optimal Resource Allocation for Early Stopping-based Neural Architecture Search Methods. AutoML (2023)
Chi Wang, Xueqing Liu, Ahmed Hassan Awadallah
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference. AutoML (2023)
Oleksandr Shchur, Ali Caner Türkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. AutoML (2023)
Daniel Dimanov, Colin Singleton, Shahin Rostami, Emili Balaguer-Ballester
MEOW - Multi-Objective Evolutionary Weapon Detection. AutoML (2023)
Linus Ericsson, Da Li, Timothy M. Hospedales
Better Practices for Domain Adaptation. AutoML (2023)
Yihang Shen, Carl Kingsford
Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection. AutoML (2023)
Tommie Kerssies, Joaquin Vanschoren
Neural Architecture Search for Visual Anomaly Segmentation. AutoML (2023)
Diederick Vermetten, Furong Ye, Thomas Bäck, Carola Doerr
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts. AutoML (2023)
Yash Akhauri, Mohamed S. Abdelfattah
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search. AutoML (2023)
Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer
Symbolic Explanations for Hyperparameter Optimization. AutoML (2023)
Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. AutoML (2023)

volume 228, 2023

International Conference on Automated Machine Learning, 12-15 November 2023, Hasso Plattner Institute, Potsdam, Germany. AutoML 228 (2023)

2022

volume 188, 2022

International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. AutoML 188 (2022)