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Sebastian Pineda Arango

6 accepted papers

2025

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables

Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen +7
AISTATS 2025poster

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, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent a…

Cited by 0Source ↗Scholar ↗
2024

Interpretable Mesomorphic Networks for Tabular Data

Arlind Kadra, Sebastian Pineda Arango, Josif Grabocka
NeurIPS 2024poster

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 propose a new class of interpretable neural networks for tabular data that are both deep and linear at the same time (i.e.…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2024

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How

Sebastian Pineda Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka
ICLR 2024oral

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 dataset. In this paper, we propose a methodology that jointly searches for the optimal pretrained model and the hyperparame…

Cited by 14Source ↗PDF ↗Scholar ↗
2023

Deep Ranking Ensembles for Hyperparameter Optimization

Abdus Salam Khazi, Sebastian Pineda Arango, Josif Grabocka
ICLR 2023poster

Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI. Existing work in Hyperparameter Optimization (HPO) trains surrogate models for approximating the response surface of hyperparameters as a regression task. In contrast, we hypothesi…

Cited by 5Source ↗PDF ↗Scholar ↗
2022

Transformers Can Do Bayesian Inference

Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, Frank Hutter
ICLR 2022poster

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. We present Prior-Data Fitted Networks (PFNs). PFNs leverage large-scale machine learning techniques to approximate a l…

Cited by 202Source ↗PDF ↗code ↗Scholar ↗
2021

HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML

Sebastian Pineda Arango, Hadi Samer Jomaa, Martin Wistuba, Josif Grabocka
NeurIPS 2021poster

Hyperparameter optimization (HPO) is a core problem for the machine learning community and remains largely unsolved due to the significant computational resources required to evaluate hyperparameter configurations. As a result, a series of recent related works have focused on the direction of transf…

Cited by 39Source ↗code ↗Scholar ↗