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Posts by Collection
portfolio
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publications
Metabolite identification and molecular fingerprint prediction through machine learning
Published in Bioinformatics, 2012
Link: [pdf]
Recommended citation: Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu. (2013). "Metabolite identification and molecular fingerprint prediction through machine learning" Bioinformatics https://academic.oup.com/bioinformatics/article/28/18/2333/252360?login=true
Metabolite identification through machine learning—Tackling CASMI challenge using fingerID
Published in Metabolites, 2013
Link: [pdf]
Recommended citation: Huibin Shen, Nicola Zamboni, Markus Heinonen, Juho Rousu. (2013). "Metabolite identification through machine learning—Tackling CASMI challenge using fingerID" Metabolites https://www.mdpi.com/2218-1989/3/2/484
Metabolite identification through multiple kernel learning on fragmentation trees
Published in Bioinformatics, 2014
Link: [pdf]
Recommended citation: Huibin Shen, Kai Dührkop, Sebastian Böcker, Juho Rousu. (2014). "Metabolite identification through multiple kernel learning on fragmentation trees" Bioinformatics https://academic.oup.com/bioinformatics/article/30/12/i157/386855?login=true
Searching molecular structure databases with tandem mass spectra using CSI: FingerID
Published in Proceedings of the National Academy of Sciences, 2015
Link: [pdf]
Recommended citation: Kai Dührkop, Huibin Shen, Marvin Meusel, Juho Rousu, Sebastian Böcker. (2017). "Searching molecular structure databases with tandem mass spectra using CSI: FingerID" Proceedings of the National Academy of Sciences https://www.pnas.org/content/112/41/12580.short
Fast metabolite identification with input output kernel regression
Published in Bioinformatics, 2016
Link: [pdf]
Recommended citation: Céline Brouard, Huibin Shen, Kai Dührkop, Florence d'Alché-Buc, Sebastian Böcker, Juho Rousu. (2017). "Fast metabolite identification with input output kernel regression" Bioinformatics https://academic.oup.com/bioinformatics/article/32/12/i28/2288626?login=true
Soft kernel target alignment for two-stage multiple kernel learning
Published in International Conference on Discovery Science, 2016
Recommended citation: Huibin Shen, Sandor Szedmak, Céline Brouard, Juho Rousu. (2016). "Soft kernel target alignment for two-stage multiple kernel learning" International Conference on Discovery Science https://link.springer.com/chapter/10.1007/978-3-319-46307-0_27
Machine Learning for Small Molecule Identification
Published in Aalto University publication series DOCTORAL DISSERTATIONS, 25/2017, 2017
Link: [pdf]
Recommended citation: Huibin Shen (2017). "Machine Learning for Small Molecule Identification" Aalto University publication series DOCTORAL DISSERTATIONS, 25/2017 https://aaltodoc.aalto.fi/handle/123456789/24783
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Published in Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019
Recommended citation: Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cedric Archambeau, Rodolphe Jenatton. (2019). "Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning" Advances in Neural Information Processing Systems 32 (NeurIPS 2019) https://papers.nips.cc/paper/2019/hash/6ea3f1874b188558fafbab78e8c3a968-Abstract.html
A quantile-based approach to hyperparameter transfer learning
Published in Advances in Neural Information Processing Systems Workshop on Meta-Learning, 2019
Link: [pdf]
Recommended citation: David Salinas, Huibin Shen, Valerio Perrone. (2019). "A quantile-based approach to hyperparameter transfer learning" Advances in Neural Information Processing Systems Workshop on Meta-Learning http://metalearning.ml/2019/papers/metalearn2019-salinas.pdf
A Quantile-based Approach for Hyperparameter Transfer Learning
Published in Proceedings of the 37th International Conference on Machine Learning, 2020
Link: [pdf]
Recommended citation: David Salinas, Huibin Shen, Valerio Perrone. (2020). "A Quantile-based Approach for Hyperparameter Transfer Learning" Proceedings of the 37th International Conference on Machine Learning (ICML 2020) http://proceedings.mlr.press/v119/salinas20a.html
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization
Published in arXiv, 2020
Recommended citation: 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. (2020). "Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization" arXiv preprint arXiv:2012.08489 https://arxiv.org/pdf/2012.08489
talks
PhD thesis defense presentation
Published:
teaching
T-61.5060 Algorithmic Methods of Data Mining
Graduate course, Aalto University, Department of Computer Science, 2013
Teaching assistant to Prof. Aristides Gionis.
ICS-E4030 Kernel Methods in Machine Learning
Graduate course, Aalto University, Department of Computer Science, 2014
Teaching assistant to Prof. Juho Rousu.
ICS-E4030 Kernel Methods in Machine Learning
Graduate course, Aalto University, Department of Computer Science, 2015
Teaching assistant to Prof. Juho Rousu.
ICS-E4030 Kernel Methods in Machine Learning
Graduate course, Aalto University, Department of Computer Science, 2016
Teaching assistant to Prof. Juho Rousu.