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Difan Deng
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2020 – today
- 2024
- [j4]Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer:
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Trans. Mach. Learn. Res. 2024 (2024) - [i8]Difan Deng, Marius Lindauer:
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. CoRR abs/2406.05088 (2024) - 2023
- [j3]Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer:
MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer:
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data. Mining. Knowl. Discov. 13(2) (2023) - [i7]Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer:
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. CoRR abs/2306.08107 (2023) - 2022
- [j1]Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter:
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. J. Mach. Learn. Res. 23: 54:1-54:9 (2022) - [c2]Difan Deng, Marius Lindauer:
Searching in the Forest for Local Bayesian Optimization. Meta-Knowledge Transfer @ ECML/PKDD 2022: 38-50 - [c1]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) 2022: 664-680 - [i6]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. CoRR abs/2205.05511 (2022) - 2021
- [i5]Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo, Guilherme Miotto, Simon Schrodi, Andre Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter:
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. CoRR abs/2105.01015 (2021) - [i4]Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer:
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. CoRR abs/2107.05847 (2021) - [i3]Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter:
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. CoRR abs/2109.09831 (2021) - [i2]Difan Deng, Marius Lindauer:
Searching in the Forest for Local Bayesian Optimization. CoRR abs/2111.05834 (2021) - 2020
- [i1]Noor H. Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter:
Squirrel: A Switching Hyperparameter Optimizer. CoRR abs/2012.08180 (2020)
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last updated on 2024-10-07 22:10 CEST by the dblp team
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