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ORCIDSebastian Pineda-Arango
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2020 – today
- 2024
[c6]Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka:
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. ICLR 2024- [i11]Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Türkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda-Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang:
Chronos: Learning the Language of Time Series. CoRR abs/2403.07815 (2024) - [i10]Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka:
Dynamic Post-Hoc Neural Ensemblers. CoRR abs/2410.04520 (2024) - [i9]Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka:
Ensembling Finetuned Language Models for Text Classification. CoRR abs/2410.19889 (2024) - 2023
- [c5]Abdus Salam Khazi, Sebastian Pineda-Arango, Josif Grabocka:
Deep Ranking Ensembles for Hyperparameter Optimization. ICLR 2023 - [c4]Sebastian Pineda-Arango
, Josif Grabocka:
Deep Pipeline Embeddings for AutoML. KDD 2023: 1907-1919 - [i8]Abdus Salam Khazi, Sebastian Pineda-Arango, Josif Grabocka:
Deep Ranking Ensembles for Hyperparameter Optimization. CoRR abs/2303.15212 (2023) - [i7]Arlind Kadra, Sebastian Pineda-Arango, Josif Grabocka:
Breaking the Paradox of Explainable Deep Learning. CoRR abs/2305.13072 (2023) - [i6]Sebastian Pineda-Arango, Josif Grabocka:
Deep Pipeline Embeddings for AutoML. CoRR abs/2305.14009 (2023) - [i5]Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka:
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. CoRR abs/2306.03828 (2023) - 2022
- [c3]Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter:
Transformers Can Do Bayesian Inference. ICLR 2022 - 2021
- [c2]Sebastian Pineda-Arango, Felix Heinrich, Kiran Madhusudhanan, Lars Schmidt-Thieme:
Multimodal Meta-Learning for Time Series Regression. AALTD@ECML/PKDD 2021: 123-138 - [c1]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 - [i4]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) - [i3]Sebastian Pineda-Arango, Felix Heinrich, Kiran Madhusudhanan, Lars Schmidt-Thieme:
Multimodal Meta-Learning for Time Series Regression. CoRR abs/2108.02842 (2021) - [i2]Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter:
Transformers Can Do Bayesian Inference. CoRR abs/2112.10510 (2021) - 2020
- [i1]Sebastian Pineda-Arango, David Obando-Paniagua, Alperen Dedeoglu, Philip Kurzendörfer, Friedemann Schestag, Randolf Scholz:
Improving Sample Efficiency with Normalized RBF Kernels. CoRR abs/2007.15397 (2020)
Coauthor Index
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