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AutoML

ML/AI Core
97 papers · 2016–2025

AutoML (International Conference on Automated Machine Learning) covers automated machine learning, including neural architecture search, hyperparameter optimization, and meta-learning.

Papers per year

9
20
24
19
25
'25

Top authors

Frank Hutter 9 papers
Marius Lindauer 6 papers
Bernd Bischl 5 papers
Lennart Schneider 4 papers
Aaron Klein 4 papers
Nick Erickson 3 papers
Huibin Shen 3 papers
Martin Hirzel 3 papers
Matthias Feurer 3 papers
David Salinas 3 papers

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their counts at this venue are combined. How identity is handled.

Newest papers

Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection 2023
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search 2023
AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks 2023
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference 2023
Neural Architecture Search for Visual Anomaly Segmentation 2023
ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments 2023
Exploiting Network Compressibility and Topology in Zero-Cost NAS 2023
Searching for Fairer Machine Learning Ensembles 2023
Learning Activation Functions for Sparse Neural Networks 2023
Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection 2023
“No Free Lunch” in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization 2023
AutoRL Hyperparameter Landscapes 2023
Optimal Resource Allocation for Early Stopping-based Neural Architecture Search Methods 2023
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization 2023
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML 2023
AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting 2023
Balanced Mixture of Supernets for Learning the CNN Pooling Architecture 2023
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts 2023
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization 2023
MEOW - Multi-Objective Evolutionary Weapon Detection 2023
All AutoML 2025 papers →