AutoML 2023 Conference Submissions

OFA³: Automatic selection of the best non-dominated sub-networks for ensembles
Rafael Claro Ito, Emely Pujolli da Silva, Fernando J. Von Zuben
- 21 Mar 2023 (modified: 05 Sept 2023)
- Submitted to AutoML 2023
- Readers: Everyone
Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective
Xuechen Zhang, Mingchen Li, Vala Vakilian, Jiasi Chen, Christos Thrampoulidis, Samet Oymak
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 Workshop
- Readers: Everyone
Exploiting Network Compressibility and Topology in Zero-Cost NAS
Lichuan Xiang, Rosco Hunter, Minghao Xu, Łukasz Dudziak, Hongkai Wen
- Published: 16 May 2023, Last Modified: 18 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
ELENAS: Elementary Neural Architecture Search
Lukas Aichberger, Günter Klambauer
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 Workshop
- Readers: Everyone
“No Free Lunch” in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization
Wuyang Chen, Wei Huang, Zhangyang Wang
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
On the selection of neural architectures from a supernet
Gabriel Meyer-Lee, Nick Cheney
- Published: 16 May 2023, Last Modified: 15 Sept 2023
- AutoML 2023 Workshop
- Readers: Everyone
Balanced Mixture of Supernets for Learning the CNN Pooling Architecture
Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Optimal Resource Allocation for Early Stopping-based Neural Architecture Search Methods
Marcel Aach, Eray Inanc, Rakesh Sarma, Morris Riedel, Andreas Lintermann
- Published: 16 May 2023, Last Modified: 05 Oct 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
Chi Wang, Xueqing Liu, Ahmed Hassan Awadallah
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
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
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Symbolic Explanations for Hyperparameter Optimization
Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
MEOW - Multi-Objective Evolutionary Weapon Detection
Daniel Dimanov, Colin Singleton, Shahin Rostami, Emili Balaguer-Ballester
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure
Lennart Oswald Purucker, Joeran Beel
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Poisson Process for Bayesian Optimization
Xiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, Dacheng Tao
- Published: 16 May 2023, Last Modified: 29 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Oversampling to Repair Bias and Imbalance Simultaneously
Martin Hirzel, Parikshit Ram
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 Workshop
- Readers: Everyone
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search
Yash Akhauri, Mohamed S Abdelfattah
- Published: 16 May 2023, Last Modified: 26 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Better Practices for Domain Adaptation
Linus Ericsson, Da Li, Timothy Hospedales
- Published: 16 May 2023, Last Modified: 08 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Neural Architecture Search for Visual Anomaly Segmentation
Tommie Kerssies, Joaquin Vanschoren
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Differentiable Architecture Search: a One-Shot Method?
Jovita Lukasik, Jonas Geiping, Michael Moeller, Margret Keuper
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 Workshop
- Readers: Everyone
AutoRL Hyperparameter Landscapes
Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Learning Activation Functions for Sparse Neural Networks
Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
Efficient Multi-stage Inference on Tabular Data
Daniel Johnson, Igor L. Markov
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 Workshop
- Readers: Everyone
Searching for Fairer Machine Learning Ensembles
Michael Feffer, Martin Hirzel, Samuel C Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone
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
- Published: 16 May 2023, Last Modified: 05 Sept 2023
- AutoML 2023 MainTrack
- Readers: Everyone