Huibin Shen

According to our database, Huibin Shen authored at least 20 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Chronos: Learning the Language of Time Series.

Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Türkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda-Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang

CoRR, 2024

2023

Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation.

Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas, Aaron Klein

CoRR, 2023

Cross-Frequency Time Series Meta-Forecasting.

Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Karthick Gopalswamy

CoRR, 2023

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting.

Oleksandr Shchur, Ali Caner Türkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang

Proceedings of the International Conference on Automated Machine Learning, 2023

2022

Automatic Termination for Hyperparameter Optimization.

Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau

Proceedings of the International Conference on Automated Machine Learning, 2022

2021

Overfitting in Bayesian Optimization: an empirical study and early-stopping solution.

Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau

CoRR, 2021

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization.

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 W. Seeger, Cédric Archambeau

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization.

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 W. Seeger, Cédric Archambeau

CoRR, 2020

Amazon SageMaker Autopilot: a white box AutoML solution at scale.

Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar S. Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Liénart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias W. Seeger, Bhaskar Dutt, Laurence Rouesnel

CoRR, 2020

Amazon SageMaker Autopilot: a white box AutoML solution at scale.

Piali Das, Nikita Ivkin, Tanya Bansal, Laurence Rouesnel, Philip Gautier, Zohar S. Karnin, Leo Dirac, Lakshmi Ramakrishnan, Andre Perunicic, Iaroslav Shcherbatyi, Wilton Wu, Aida Zolic, Huibin Shen, Amr Ahmed, Fela Winkelmolen, Miroslav Miladinovic, Cédric Archambeau, Alex Tang, Bhaskar Dutt, Patricia Grao, Kumar Venkateswar

Proceedings of the Fourth Workshop on Data Management for End-To-End Machine Learning, 2020

A Quantile-based Approach for Hyperparameter Transfer Learning.

David Salinas, Huibin Shen, Valerio Perrone

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

A Copula approach for hyperparameter transfer learning.

David Salinas, Huibin Shen, Valerio Perrone

CoRR, 2019

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning.

Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton

CoRR, 2019

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning.

Valerio Perrone, Huibin Shen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Machine Learning for Small Molecule Identification.

Huibin Shen

PhD thesis, 2017

Critical Assessment of Small Molecule Identification 2016: automated methods.

Emma Schymanski, Christoph Ruttkies, Martin Krauss, Céline Brouard, Tobias Kind, Kai Dührkop, Felicity Allen, Arpana Vaniya, Dries Verdegem, Sebastian Böcker, Juho Rousu, Huibin Shen, Hiroshi Tsugawa, Tanvir Sajed, Oliver Fiehn, Bart Ghesquière, Steffen Neumann

J. Cheminformatics, 2017

2016

Fast metabolite identification with Input Output Kernel Regression.

Céline Brouard, Huibin Shen, Kai Dührkop, Florence d'Alché-Buc, Sebastian Böcker, Juho Rousu

Bioinform., 2016

Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning.

Huibin Shen, Sándor Szedmák, Céline Brouard, Juho Rousu

Proceedings of the Discovery Science - 19th International Conference, 2016

2014

Metabolite identification through multiple kernel learning on fragmentation trees.

Huibin Shen, Kai Dührkop, Sebastian Böcker, Juho Rousu

Bioinform., 2014

2012

Metabolite identification and molecular fingerprint prediction through machine learning.

Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu

Bioinform., 2012