Karakulev A; Zachariah D; Singh P
Adaptive Parameter-Free Robust Learning using Latent Bernoulli Variables Conference
arXiv preprint, vol. arXiv:2312.00585, 2023.
Abstract | Links | BibTeX | Tags: Bayesian Inference, Deep Learning, Optimization, Robust Learning
@conference{nokey,
title = {Adaptive Parameter-Free Robust Learning using Latent Bernoulli Variables},
author = {Aleksandr Karakulev and Dave Zachariah and Prashant Singh},
url = {https://arxiv.org/abs/2312.00585},
doi = {https://doi.org/10.48550/arXiv.2312.00585},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
booktitle = {arXiv preprint},
volume = {arXiv:2312.00585},
abstract = {We present an efficient parameter-free approach for statistical learning from corrupted training sets. We identify corrupted and non-corrupted samples using latent Bernoulli variables, and therefore formulate the robust learning problem as maximization of the likelihood where latent variables are marginalized out. The resulting optimization problem is solved via variational inference using an efficient Expectation-Maximization based method. The proposed approach improves over the state-of-the-art by automatically inferring the corruption level and identifying outliers, while adding minimal computational overhead. We demonstrate our robust learning method on a wide variety of machine learning tasks including online learning and deep learning where it exhibits ability to adapt to different levels of noise and attain high prediction accuracy.},
keywords = {Bayesian Inference, Deep Learning, Optimization, Robust Learning},
pubstate = {published},
tppubtype = {conference}
}
We present an efficient parameter-free approach for statistical learning from corrupted training sets. We identify corrupted and non-corrupted samples using latent Bernoulli variables, and therefore formulate the robust learning problem as maximization of the likelihood where latent variables are marginalized out. The resulting optimization problem is solved via variational inference using an efficient Expectation-Maximization based method. The proposed approach improves over the state-of-the-art by automatically inferring the corruption level and identifying outliers, while adding minimal computational overhead. We demonstrate our robust learning method on a wide variety of machine learning tasks including online learning and deep learning where it exhibits ability to adapt to different levels of noise and attain high prediction accuracy. Fronk C; Yun J; Singh P; Petzold. L
Bayesian polynomial neural networks and polynomial neural ordinary differential equations. Journal Article
In: arXiv preprint, 2023.
Links | BibTeX | Tags: Bayesian Inference, Deep Learning, Inverse Problem, Surrogate Modeling
@article{nokey,
title = {Bayesian polynomial neural networks and polynomial neural ordinary differential equations.},
author = {Colby Fronk and Jaewoong Yun and Prashant Singh and Linda Petzold.},
doi = {arXiv:2308.10892},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
journal = {arXiv preprint},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Jiang R; Singh P; Wrede F; Hellander A; Petzold L
Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods Journal Article
In: PLoS computational biology, vol. 18, no. 1, pp. e1009830, 2022.
Links | BibTeX | Tags: Bayesian Inference, Inverse Problem, Model Identification
@article{jiang2022identification,
title = {Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods},
author = {Richard Jiang and Prashant Singh and Fredrik Wrede and Andreas Hellander and Linda Petzold},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009830},
doi = {https://doi.org/10.1371/journal.pcbi.1009830},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {PLoS computational biology},
volume = {18},
number = {1},
pages = {e1009830},
publisher = {Public Library of Science San Francisco, CA USA},
keywords = {Bayesian Inference, Inverse Problem, Model Identification},
pubstate = {published},
tppubtype = {article}
}
Wrede F; Eriksson R; Jiang R; Petzold L; Engblom S; Hellander A; Singh P
Robust and integrative Bayesian neural networks for likelihood-free parameter inference Proceedings Article
In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE 2022.
Links | BibTeX | Tags: Bayesian Inference, Deep Learning, Inverse Problem, Surrogate Modeling
@inproceedings{wrede2022robust,
title = {Robust and integrative Bayesian neural networks for likelihood-free parameter inference},
author = {Fredrik Wrede and Robin Eriksson and Richard Jiang and Linda Petzold and Stefan Engblom and Andreas Hellander and Prashant Singh},
url = {https://ieeexplore.ieee.org/abstract/document/9892800
https://arxiv.org/pdf/2102.06521},
doi = {https://doi.org/10.1109/IJCNN55064.2022.9892800},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
pages = {1--10},
organization = {IEEE},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Coulier A; Singh P; Sturrock M; Hellander A
Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation Journal Article
In: PLOS Computational Biology, vol. 18, no. 12, pp. e1010683, 2022.
Links | BibTeX | Tags: Bayesian Inference, Inverse Problem
@article{coulier2022systematic,
title = {Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation},
author = {Adrien Coulier and Prashant Singh and Marc Sturrock and Andreas Hellander},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010683},
doi = {https://doi.org/10.1371/journal.pcbi.1010683},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {PLOS Computational Biology},
volume = {18},
number = {12},
pages = {e1010683},
publisher = {Public Library of Science San Francisco, CA USA},
keywords = {Bayesian Inference, Inverse Problem},
pubstate = {published},
tppubtype = {article}
}
Jiang R M; Wrede F; Singh P; Hellander A; Petzold L R
Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators Journal Article
In: BMC bioinformatics, vol. 22, no. 1, pp. 1–17, 2021.
Links | BibTeX | Tags: Bayesian Inference, Inverse Problem, Surrogate Modeling
@article{jiang2021accelerated,
title = {Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators},
author = {Richard M Jiang and Fredrik Wrede and Prashant Singh and Andreas Hellander and Linda R Petzold},
url = {https://link.springer.com/article/10.1186/s12859-021-04255-9},
doi = {https://doi.org/10.1186/s12859-021-04255-9},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {BMC bioinformatics},
volume = {22},
number = {1},
pages = {1--17},
publisher = {BioMed Central},
keywords = {Bayesian Inference, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Akesson M; Singh P; Wrede F; Hellander A
Convolutional neural networks as summary statistics for approximate bayesian computation Journal Article
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
Links | BibTeX | Tags: Bayesian Inference, Deep Learning, Inverse Problem
@article{akesson2021convolutional,
title = {Convolutional neural networks as summary statistics for approximate bayesian computation},
author = {Mattias Akesson and Prashant Singh and Fredrik Wrede and Andreas Hellander},
url = {https://ieeexplore.ieee.org/abstract/document/9525290},
doi = {https://doi.org/10.1109/TCBB.2021.3108695},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {IEEE},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem},
pubstate = {published},
tppubtype = {article}
}
Jiang R; Jacob B; Geiger M; Matthew S; Rumsey B; Singh P; Wrede F; Yi T; Drawert B; Hellander A; others
Epidemiological modeling in stochss live! Journal Article
In: Bioinformatics, vol. 37, no. 17, pp. 2787–2788, 2021.
Links | BibTeX | Tags: Bayesian Inference, Software
@article{jiang2021epidemiological,
title = {Epidemiological modeling in stochss live!},
author = {Richard Jiang and Bruno Jacob and Matthew Geiger and Sean Matthew and Bryan Rumsey and Prashant Singh and Fredrik Wrede and Tau-Mu Yi and Brian Drawert and Andreas Hellander and others},
url = {https://academic.oup.com/bioinformatics/article/37/17/2787/6123781
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab061/40342592/btab061.pdf},
doi = {https://doi.org/10.1093/bioinformatics/btab061},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {17},
pages = {2787--2788},
publisher = {Oxford University Press},
keywords = {Bayesian Inference, Software},
pubstate = {published},
tppubtype = {article}
}
Singh P; Wrede F; Hellander A
Scalable machine learning-assisted model exploration and inference using Sciope Journal Article
In: Bioinformatics, vol. 37, no. 2, pp. 279–281, 2021.
Links | BibTeX | Tags: Bayesian Inference, Deep Learning, Inverse Problem, Optimization, Software, Surrogate Modeling
@article{singh2021scalable,
title = {Scalable machine learning-assisted model exploration and inference using Sciope},
author = {Prashant Singh and Fredrik Wrede and Andreas Hellander},
url = {https://academic.oup.com/bioinformatics/article/37/2/279/5876021},
doi = {https://doi.org/10.1093/bioinformatics/btaa673},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {2},
pages = {279--281},
publisher = {Oxford University Press},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Optimization, Software, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Hellander A
Hyperparameter optimization for approximate Bayesian computation Proceedings Article
In: 2018 Winter Simulation Conference (WSC), pp. 1718–1729, IEEE 2018.
Links | BibTeX | Tags: Bayesian Inference, Inverse Problem, Optimization
@inproceedings{singh2018hyperparameter,
title = {Hyperparameter optimization for approximate Bayesian computation},
author = {Prashant Singh and Andreas Hellander},
url = {https://ieeexplore.ieee.org/abstract/document/8632304
},
doi = {https://doi.org/10.1109/WSC.2018.8632304},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 Winter Simulation Conference (WSC)},
pages = {1718--1729},
organization = {IEEE},
keywords = {Bayesian Inference, Inverse Problem, Optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Hellander A
Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference Proceedings Article
In: ESM 2018, October 24--26, Ghent, Belgium, pp. 22–27, EUROSIS 2018.
BibTeX | Tags: Bayesian Inference, Inverse Problem, Optimization
@inproceedings{singh2018multi,
title = {Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference},
author = {Prashant Singh and Andreas Hellander},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {ESM 2018, October 24--26, Ghent, Belgium},
pages = {22--27},
organization = {EUROSIS},
keywords = {Bayesian Inference, Inverse Problem, Optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
Adaptive Parameter-Free Robust Learning using Latent Bernoulli Variables Conference
arXiv preprint, vol. arXiv:2312.00585, 2023.
Bayesian polynomial neural networks and polynomial neural ordinary differential equations. Journal Article
In: arXiv preprint, 2023.
Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods Journal Article
In: PLoS computational biology, vol. 18, no. 1, pp. e1009830, 2022.
Robust and integrative Bayesian neural networks for likelihood-free parameter inference Proceedings Article
In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE 2022.
Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation Journal Article
In: PLOS Computational Biology, vol. 18, no. 12, pp. e1010683, 2022.
Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators Journal Article
In: BMC bioinformatics, vol. 22, no. 1, pp. 1–17, 2021.
Convolutional neural networks as summary statistics for approximate bayesian computation Journal Article
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
Epidemiological modeling in stochss live! Journal Article
In: Bioinformatics, vol. 37, no. 17, pp. 2787–2788, 2021.
Scalable machine learning-assisted model exploration and inference using Sciope Journal Article
In: Bioinformatics, vol. 37, no. 2, pp. 279–281, 2021.
Hyperparameter optimization for approximate Bayesian computation Proceedings Article
In: 2018 Winter Simulation Conference (WSC), pp. 1718–1729, IEEE 2018.
Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference Proceedings Article
In: ESM 2018, October 24--26, Ghent, Belgium, pp. 22–27, EUROSIS 2018.