| Chronos: Learning the language of time series AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ... arXiv preprint arXiv:2403.07815, 2024 | 202 | 2024 |
| Transformers Can Do Bayesian Inference S Müller, N Hollmann, SP Arango, J Grabocka, F Hutter International Conference on Learning Representations, 2021 | 166 | 2021 |
| HPO-B: A large-scale reproducible benchmark for black-box HPO based on openml S Pineda Arango, HS Jomaa, M Wistuba, J Grabocka NeurIPS Datasets and Benchmarks, 2021 | 50* | 2021 |
| Multimodal meta-learning for time series regression SP Arango, F Heinrich, K Madhusudhanan, L Schmidt-Thieme Advanced Analytics and Learning on Temporal Data: 6th ECML PKDD Workshop …, 2021 | 23 | 2021 |
| Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How SP Arango, F Ferreira, A Kadra, F Hutter, J Grabocka International Conference on Learning Representations, 2024 | 12* | 2024 |
| HPO-RL-Bench: A zero-cost benchmark for HPO in reinforcement learning G Shala, SP Arango, A Biedenkapp, F Hutter, J Grabocka AutoML Conference 2024 (ABCD Track), 2024 | 7* | 2024 |
| Deep Ranking Ensembles for Hyperparameter Optimization AS Khazi*, S Pineda-Arango*, J Grabocka International Conference on Learning Representations, 2023 | 6 | 2023 |
| Transfer learning for bayesian hpo with end-to-end landmark meta-features HS Jomaa, SP Arango, L Schmidt-Thieme, J Grabocka Fifth Workshop on Meta-Learning at the Conference on Neural Information …, 2021 | 6 | 2021 |
| Improving sample efficiency with normalized rbf kernels S Pineda-Arango, D Obando-Paniagua, A Dedeoglu, P Kurzendörfer, ... arXiv preprint arXiv:2007.15397, 2020 | 5 | 2020 |
| Deep Pipeline Embeddings for AutoML S Pineda Arango, J Grabocka ACM SIGKDD International Conference on Knowledge Discovery and Data Mining …, 2023 | 4 | 2023 |
| Interpretable Mesomorphic Networks for Tabular Data A Kadra, S Pineda Arango, J Grabocka Advances in Neural Information Processing Systems 37, 31759-31787, 2025 | 3* | 2025 |
| Quick-tune-tool: A practical tool and its user guide for automatically finetuning pretrained models I Rapant, L Purucker, F Ferreira, SP Arango, A Kadra, J Grabocka, ... AutoML Conference 2024 (Workshop Track), 2024 | 1 | 2024 |
| Ensembling Finetuned Language Models for Text Classification SP Arango, M Janowski, L Purucker, A Zela, F Hutter, J Grabocka arXiv preprint arXiv:2410.19889, 2024 | | 2024 |
| Dynamic Post-Hoc Neural Ensemblers SP Arango, M Janowski, L Purucker, A Zela, F Hutter, J Grabocka arXiv preprint arXiv:2410.04520, 2024 | | 2024 |
| ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables SP Arango, P Mercado, S Kapoor, AF Ansari, L Stella, H Shen, ... The 28th International Conference on Artificial Intelligence and Statistics, 0 | | |
| Bayesian Optimization with a Neural Network Meta-learned on Synthetic Data Only S Müller, SP Arango, M Feurer, J Grabocka, F Hutter Sixth Workshop on Meta-Learning at the Conference on Neural Information …, 0 | | |