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Publication search results
found 76 matches
- 2023
- Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov:
Variational Boosted Soft Trees. AISTATS 2023: 5787-5801 - Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella:
PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR 2023 - Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka:
Scaling Laws for Hyperparameter Optimization. NeurIPS 2023 - Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka:
Deep Power Laws for Hyperparameter Optimization. CoRR abs/2302.00441 (2023) - Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov:
Variational Boosted Soft Trees. CoRR abs/2302.10706 (2023) - Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella:
Renate: A Library for Real-World Continual Learning. CoRR abs/2304.12067 (2023) - Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella:
Continual Learning with Low Rank Adaptation. CoRR abs/2311.17601 (2023) - 2022
- Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea:
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. AAAI 2022: 10228-10237 - David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau:
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML 2022: 16/1-23 - Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Continual Learning with Transformers for Image Classification. CVPR Workshops 2022: 3773-3780 - Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Memory Efficient Continual Learning with Transformers. NeurIPS 2022 - Martin Wistuba, Arlind Kadra, Josif Grabocka:
Supervising the Multi-Fidelity Race of Hyperparameter Configurations. NeurIPS 2022 - Martin Wistuba, Arlind Kadra, Josif Grabocka:
Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning. CoRR abs/2202.09774 (2022) - Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau:
Memory Efficient Continual Learning for Neural Text Classification. CoRR abs/2203.04640 (2022) - Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Continual Learning with Transformers for Image Classification. CoRR abs/2206.14085 (2022) - 2021
- Radu Marinescu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea:
Searching for Machine Learning Pipelines Using a Context-Free Grammar. AAAI 2021: 8902-8911 - Arunima Chaudhary, Alayt Issak, Kiran Kate, Yannis Katsis, Abel N. Valente, Dakuo Wang, Alexandre V. Evfimievski, Sairam Gurajada, Ban Kawas, A. Cristiano I. Malossi, Lucian Popa, Tejaswini Pedapati, Horst Samulowitz, Martin Wistuba, Yunyao Li:
AutoText: An End-to-End AutoAI Framework for Text. AAAI 2021: 16001-16003 - Hoang Thanh Lam, Beat Buesser, Hong Min, Tran Ngoc Minh, Martin Wistuba, Udayan Khurana, Gregory Bramble, Theodoros Salonidis, Dakuo Wang, Horst Samulowitz:
Automated Data Science for Relational Data. ICDE 2021: 2689-2692 - Martin Wistuba, Josif Grabocka:
Few-Shot Bayesian Optimization with Deep Kernel Surrogates. ICLR 2021 - Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang:
Hardware-Aware Neural Architecture Search: Survey and Taxonomy. IJCAI 2021: 4322-4329 - Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka:
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. NeurIPS Datasets and Benchmarks 2021 - Martin Wistuba, Josif Grabocka:
Few-Shot Bayesian Optimization with Deep Kernel Surrogates. CoRR abs/2101.07667 (2021) - Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang:
A Comprehensive Survey on Hardware-Aware Neural Architecture Search. CoRR abs/2101.09336 (2021) - Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka:
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. CoRR abs/2106.06257 (2021) - 2020
- Martin Wistuba, Tejaswini Pedapati:
Learning to Rank Learning Curves. ICML 2020: 10303-10312 - Djallel Bouneffouf, Charu C. Aggarwal, Thanh Hoang, Udayan Khurana, Horst Samulowitz, Beat Buesser, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray:
Survey on Automated End-to-End Data Science? IJCNN 2020: 1-9 - Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati:
Automation of Deep Learning - Theory and Practice. ICMR 2020: 5-6 - Martin Wistuba:
XferNAS: Transfer Neural Architecture Search. ECML/PKDD (3) 2020: 247-262 - Martin Wistuba, Tejaswini Pedapati:
Learning to Rank Learning Curves. CoRR abs/2006.03361 (2020) - 2019
- Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba:
Optimal Exploitation of Clustering and History Information in Multi-armed Bandit. IJCAI 2019: 2016-2022
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