61 citations · 69 across the 9 of their papers we have counts for
9 papers
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor +10
Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, co…
Ensembling Finetuned Language Models for Text Classification
Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker +3
Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks. Text classification is one of these tasks for which many pre…
Regularized Neural Ensemblers
Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker +3
Ensemble methods are known for enhancing the accuracy and robustness of machine learning models by combining multiple base learners. However, standard approaches like greedy or ran…
Chronos: Learning the Language of Time Series
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen +15
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a f…
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
Sebastian Pineda Arango, Fabio Ferreira, Arlind Kadra +2
With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dat…
Deep Pipeline Embeddings for AutoML
Sebastian Pineda Arango, Josif Grabocka
Automated Machine Learning (AutoML) is a promising direction for democratizing AI by automatically deploying Machine Learning systems with minimal human expertise. The core technic…
Interpretable Mesomorphic Networks for Tabular Data
Arlind Kadra, Sebastian Pineda Arango, Josif Grabocka
Even though neural networks have been long deployed in applications involving tabular data, still existing neural architectures are not explainable by design. In this paper, we pro…
Deep Ranking Ensembles for Hyperparameter Optimization
Abdus Salam Khazi, Sebastian Pineda Arango, Josif Grabocka
Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI. Existing work in Hyperparameter Optimization (HPO) trains su…
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Sebastian Pineda Arango, Hadi S. Jomaa, Martin Wistuba +1
Hyperparameter optimization (HPO) is a core problem for the machine learning community and remains largely unsolved due to the significant computational resources required to evalu…