no code implementations • 4 Feb 2023 • Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Karthick Gopalswamy
Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting.
1 code implementation • 16 Apr 2021 • Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias Seeger, Cedric Archambeau
Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time.
no code implementations • 15 Dec 2020 • Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias Seeger, Bhaskar Dutt, Laurence Rouesnel
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
no code implementations • 15 Dec 2020 • Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias Seeger, Cédric Archambeau
To democratize access to machine learning systems, it is essential to automate the tuning.
no code implementations • ICML 2020 • David Salinas, Huibin Shen, Valerio Perrone
In this work, we introduce a novel approach to achieve transfer learning across different \emph{datasets} as well as different \emph{objectives}.
no code implementations • NeurIPS 2019 • Valerio Perrone, Huibin Shen, Matthias Seeger, Cedric Archambeau, Rodolphe Jenatton
Despite its simplicity, we show that our approach considerably boosts BO by reducing the size of the search space, thus accelerating the optimization of a variety of black-box optimization problems.
no code implementations • 25 Sep 2019 • David Salinas, Huibin Shen, Valerio Perrone
In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different metrics.