6 papers
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…
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…
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…
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…
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…
Transformers Can Do Bayesian Inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango +2
Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty…