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Publication search results
found 46 matches
- 2023
- Chi Wang, Xueqing Liu, Ahmed Hassan Awadallah:
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference. AutoML 2023: 21/1-17 - Marcel Aach, Eray Inanc, Rakesh Sarma, Morris Riedel, Andreas Lintermann:
Optimal Resource Allocation for Early Stopping-based Neural Architecture Search Methods. AutoML 2023: 12/1-17 - Yash Akhauri, Mohamed S. Abdelfattah:
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search. AutoML 2023: 23/1-23 - Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. AutoML 2023: 6/1-50 - Wuyang Chen, Wei Huang, Zhangyang Wang:
"No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization. AutoML 2023: 14/1-29 - Daniel Dimanov, Colin Singleton, Shahin Rostami, Emili Balaguer-Ballester:
MEOW - Multi-Objective Evolutionary Weapon Detection. AutoML 2023: 5/1-20 - Linus Ericsson, Da Li, Timothy M. Hospedales:
Better Practices for Domain Adaptation. AutoML 2023: 4/1-25 - Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar:
Searching for Fairer Machine Learning Ensembles. AutoML 2023: 17/1-19 - Iordanis Fostiropoulos, Laurent Itti:
ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments. AutoML 2023: 19/1-15 - Tommie Kerssies, Joaquin Vanschoren:
Neural Architecture Search for Visual Anomaly Segmentation. AutoML 2023: 20/1-14 - 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: 11/1-17 - Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer:
Learning Activation Functions for Sparse Neural Networks. AutoML 2023: 16/1-19 - 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: 22/1-22 - Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer:
AutoRL Hyperparameter Landscapes. AutoML 2023: 13/1-27 - José Manuel Navarro, Alexis Huet, Dario Rossi:
Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection. AutoML 2023: 24/1-19 - Lennart Oswald Purucker, Joeran Beel:
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure. AutoML 2023: 1/1-23 - 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: 10/1-34 - Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli:
Balanced Mixture of Supernets for Learning the CNN Pooling Architecture. AutoML 2023: 8/1-23 - Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer:
Symbolic Explanations for Hyperparameter Optimization. AutoML 2023: 2/1-22 - 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: 9/1-21 - Yihang Shen, Carl Kingsford:
Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection. AutoML 2023: 15/1-27 - 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: 7/1-14 - Xiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, Dacheng Tao:
Poisson Process for Bayesian Optimization. AutoML 2023: 3/1-20 - Lichuan Xiang, Rosco Hunter, Minghao Xu, Lukasz Dudziak, Hongkai Wen:
Exploiting Network Compressibility and Topology in Zero-Cost NAS. AutoML 2023: 18/1-14 - Aleksandra Faust, Roman Garnett, Colin White, Frank Hutter, Jacob R. Gardner:
International Conference on Automated Machine Learning, 12-15 November 2023, Hasso Plattner Institute, Potsdam, Germany. Proceedings of Machine Learning Research 224, PMLR 2023 [contents] - 2022
- Mehdi Bahrami, Wei-Peng Chen, Lei Liu, Mukul R. Prasad:
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML. AutoML 2022: 11/1-26 - Trapit Bansal, Salaheddin Alzubi, Tong Wang, Jay-Yoon Lee, Andrew McCallum:
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning. AutoML 2022: 19/1-18 - Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan, Hai Li, Vikas Chandra, Yiran Chen:
ScaleNAS: Multi-Path One-Shot NAS for Scale-Aware High-Resolution Representation. AutoML 2022: 15/1-18 - Duc N. M. Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang:
AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks. AutoML 2022: 4/1-16 - Kevin Alexander Laube, Maximus Mutschler, Andreas Zell:
What to expect of hardware metric predictors in NAS. AutoML 2022: 13/1-15
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