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publications
Gene hunting with knockoffs for hidden Markov models
Published in Biometrika, 2019
This paper develop an exact construction of knockoffs for variables distributed as hidden Markov models, and builds upon this a principled and versatile method for controlling the false discovery rate in genome-wide association studies.
Recommended citation: Sesia, Sabatti, and Candès. (2019). "Gene hunting with knockoffs for hidden Markov models." Biometrika. 106(1), pages 1-18. https://doi.org/10.1093/biomet/asy033
Rejoinder: ‘Gene hunting with knockoffs for hidden Markov models’
Published in Biometrika, 2019
Discussion of ‘Gene hunting with knockoffs for hidden Markov models’.
Recommended citation: Sesia, Sabatti, and Candès. (2019). "Gene hunting with knockoffs for hidden Markov models." Biometrika. 106(1), pages 35-45. https://doi.org/10.1093/biomet/asy075
Deep Knockoffs
Published in Journal of the American Statistical Association, 2019
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. By building upon the existing model-X framework, we thus obtain a flexible and model-free statistical tool to perform controlled variable selection.
Recommended citation: Romano, Sesia, and Candès. (2019). "Deep Knockoffs." J. Am. Stat. Assoc.. 0(0), pages 1-12. https://doi.org/10.1080/01621459.2019.1660174
Multi-resolution localization of causal variants across the genome.
Published in Nature Communications, 2020
A knockoff-based method for the genetic mapping of complex traits at multiple resolutions, and a large-scale application to the UK Biobank data.
Recommended citation: Sesia, Bates, Katsevich, Candès, and Sabatti. (2020). "Multi-resolution localization of causal variants across the genome." Nature Commun. 11, 1093. https://doi.org/10.1038/s41467-020-14791-2
A comparison of some conformal quantile regression methods
Published in Stat, 2020
This paper compares two recent methods that combine conformal inference with quantile regression to produce locally adaptive and marginally valid prediction intervals under sample exchangeability, both theoretically and empirically.
Recommended citation: Sesia and Candès. (2020). "A comparison of some conformal quantile regression methods." Stat. 9:e261. http://dx.doi.org/10.1002/sta4.261
Causal inference in genetic trio studies
Published in Proc. Natl. Acad. Sci. U.S.A., 2020
Flexible and rigorous causal inference from genetic trio data.
Recommended citation: Bates, Sesia, Sabatti, and Candès (2020). "Causal inference in genetic trio studies." Proc. Natl. Acad. Sci. U.S.A., 117 (39) 24117-24126. https://www.pnas.org/content/early/2020/09/17/2007743117
Classification with valid and adaptive coverage
Published in Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
Model-free classification inference that can efficiently adapt to complex data distributions.
Recommended citation: Romano, Sesia, and Candès (2020). "Classification with valid and adaptive coverage." Advances in Neural Information Processing Systems 33. https://papers.nips.cc/paper/2020/hash/244edd7e85dc81602b7615cd705545f5-Abstract.html
Interpretable signal analysis with knockoffs enhances classification of bacterial Raman spectra
Published in pre-print, 2020
An application of knockoffs to signal-processing data from biophysics.
Recommended citation: Chia, Sesia, Ho, Jeffrey, Dionne, Candès, Howe (2020). "Interpretable signal analysis with knockoffs enhances classification of bacterial Raman spectra." pre-print at arXiv:2006.04937 . https://arxiv.org/abs/2006.04937
Controlling the false discovery rate in GWAS with population structure
Published in pre-print, 2020
A knockoff-based method for the genetic mapping of complex traits at multiple resolutions accounting for population structure, and a large-scale application to the UK Biobank data.
Recommended citation: Sesia, Bates, Candès, Marchini, and Sabatti (2020). "Controlling the false discovery rate in GWAS with population structure." bioRxiv. https://doi.org/10.1101/2020.08.04.236703
Testing for Outliers with Conformal p-values
Published in pre-print, 2021
This paper studies the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective.
Recommended citation: Bates, Candès, Lei, Romano, and Sesia (2021). "Testing for Outliers with Conformal p-values." arXiv. https://arxiv.org/abs/2104.08279
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Stats 195, Introduction to R
Undergraduate course, Stanford University, 2018
Course page: http://web.stanford.edu/~msesia/stats195/
Stats 390, Consulting Workshop
Graduate course, Stanford University, 2018
Stats 195, Introduction to R
Undergraduate course, Stanford University, 2020
Course page: http://web.stanford.edu/~msesia/stats195/
BUAD 310, Applied Business Statistics
Undergraduate course, University of Southern California, 2020