Zobrazeno 1 - 10
of 235
pro vyhledávání: '"SALINAS, DAVID"'
This paper introduces Mamba4Cast, a zero-shot foundation model for time series forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted Networks (PFNs), Mamba4Cast generalizes robustly across diverse time series tasks without th
Externí odkaz:
http://arxiv.org/abs/2410.09385
Autor:
Mueller, Andreas, Siems, Julien, Nori, Harsha, Salinas, David, Zela, Arber, Caruana, Rich, Hutter, Frank
Generalized Additive Models (GAMs) are widely recognized for their ability to create fully interpretable machine learning models for tabular data. Traditionally, training GAMs involves iterative learning algorithms, such as splines, boosted trees, or
Externí odkaz:
http://arxiv.org/abs/2410.04560
Autor:
Becktepe, Jannis, Dierkes, Julian, Benjamins, Carolin, Mohan, Aditya, Salinas, David, Rajan, Raghu, Hutter, Frank, Hoos, Holger, Lindauer, Marius, Eimer, Theresa
Publikováno v:
17th European Workshop on Reinforcement Learning 2024
Hyperparameters are a critical factor in reliably training well-performing reinforcement learning (RL) agents. Unfortunately, developing and evaluating automated approaches for tuning such hyperparameters is both costly and time-consuming. As a resul
Externí odkaz:
http://arxiv.org/abs/2409.18827
Autor:
Rangapuram, Syama Sundar, Gasthaus, Jan, Stella, Lorenzo, Flunkert, Valentin, Salinas, David, Wang, Yuyang, Januschowski, Tim
This paper presents non-parametric baseline models for time series forecasting. Unlike classical forecasting models, the proposed approach does not assume any parametric form for the predictive distribution and instead generates predictions by sampli
Externí odkaz:
http://arxiv.org/abs/2312.14657
Autor:
Salinas, David, Erickson, Nick
We introduce TabRepo, a new dataset of tabular model evaluations and predictions. TabRepo contains the predictions and metrics of 1310 models evaluated on 200 classification and regression datasets. We illustrate the benefit of our dataset in multipl
Externí odkaz:
http://arxiv.org/abs/2311.02971
We introduce ordered transfer hyperparameter optimisation (OTHPO), a version of transfer learning for hyperparameter optimisation (HPO) where the tasks follow a sequential order. Unlike for state-of-the-art transfer HPO, the assumption is that each t
Externí odkaz:
http://arxiv.org/abs/2306.16916
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capt
Externí odkaz:
http://arxiv.org/abs/2305.03623
Autor:
Januschowski, Tim, Gasthaus, Jan, Wang, Yuyang, Salinas, David, Flunkert, Valentin, Bohlke-Schneider, Michael, Callot, Laurent
Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organize
Externí odkaz:
http://arxiv.org/abs/2212.03523
Autor:
López, Juan L.1 (AUTHOR) jlopez@ucm.cl, Morales-Salinas, David2 (AUTHOR), Toral-Acosta, Daniel3 (AUTHOR)
Publikováno v:
Economies. Oct2024, Vol. 12 Issue 10, p269. 15p.
