AutoML 2023

24 papers

“No Free Lunch” in Neural Architectures? a Joint Analysis of Expressivity, Convergence, and Generalization Wuyang Chen, Wei Huang, Zhangyang Wang

ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments Iordanis Fostiropoulos, Laurent Itti

AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks Roque Lopez, Raoni Lourenco, Remi Rampin, Sonia Castelo, Aécio S. R. Santos, Jorge Henrique Piazentin Ono, Claudio Silva, Juliana Freire

AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting Oleksandr Shchur, Ali Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang

AutoRL Hyperparameter Landscapes Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer

Balanced Mixture of Supernets for Learning the CNN Pooling Architecture Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli

Better Practices for Domain Adaptation Linus Ericsson, Da Li, Timothy Hospedales

CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure Lennart Oswald Purucker, Joeran Beel

Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection Yihang Shen, Carl Kingsford

Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference Chi Wang, Xueqing Liu, Ahmed Hassan Awadallah

Exploiting Network Compressibility and Topology in Zero-Cost NAS Lichuan Xiang, Rosco Hunter, Minghao Xu, Łukasz Dudziak, Hongkai Wen

Learning Activation Functions for Sparse Neural Networks Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer

MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts Diederick Vermetten, Furong Ye, Thomas Bäck, Carola Doerr

MEOW - Multi-Objective Evolutionary Weapon Detection Daniel Dimanov, Colin Singleton, Shahin Rostami, Emili Balaguer-Ballester

Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection Jose Manuel Navarro, Alexis Huet, Dario Rossi

Multi-Predict: Few Shot Predictors for Efficient Neural Architecture Search Yash Akhauri, Mohamed S Abdelfattah

Neural Architecture Search for Visual Anomaly Segmentation Tommie Kerssies, Joaquin Vanschoren

Optimal Resource Allocation for Early Stopping-Based Neural Architecture Search Methods Marcel Aach, Eray Inanc, Rakesh Sarma, Morris Riedel, Andreas Lintermann

Poisson Process for Bayesian Optimization Xiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, Dacheng Tao

PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Jankovic, Ana Nikolikj, Urban Skvorc, Peter Korosec, Carola Doerr, Tome Eftimov

Q(D)O-ES: Population-Based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML Lennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger Hoos

Searching for Fairer Machine Learning Ensembles Michael Feffer, Martin Hirzel, Samuel C Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer

Symbolic Explanations for Hyperparameter Optimization Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer