Published in pre-print, 2021
This paper develops a method to increase the power of conditional testing via knockoffs by leveraging prior information in external data sets collected from different populations or measuring related outcomes.
Recommended citation: Li, Ren, Sabatti, and Sesia (2021). "Transfer learning in genome-wide association studies with knockoffs." arXiv preprint . https://arxiv.org/abs/2108.08813
Published in IEEE Journal of Biomedical and Health Informatics, 2021
An application of knockoffs to signal-processing data from biophysics.
Recommended citation: Chia, Sesia, Ho, Jeffrey, Dionne, Candès, and Howe (2021). "Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets." IEEE J. Biomed. Health. Inform. . https://doi.org/10.1109/JBHI.2021.3094873
Published in pre-print, 2021
This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across diverse environments, controlling the false discovery rate.
Recommended citation: Li, Sesia, Romano, Candès, and Sabatti (2021). "Searching for consistent associations with a multi-environment knockoff filter." arXiv. https://arxiv.org/abs/2106.04118
Published in pre-print, 2021
This paper develops efficient conformity scores for non-parametric regression.
Recommended citation: Sesia and Romano (2021). "Conformal Histogral Regression." arXiv. https://arxiv.org/abs/2105.08747
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
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
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
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
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
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
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
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
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