McGough, Witten, and Kessler (2025) Valid F-screening in linear regression. arXiv:2505.23113
Connor, Zhang, Neufeld, . . . and Stevens (2025) Bioprinted platform for parallelized screening of engineered microtissues <i>in vivo</i>. <i>Cell Stem Cell</i> 32: 858-853.
Xu, Witten, and Shojaie (2025) A unified framework for semi-parametrically efficient semi-supervised learning. arXiv:2502.17741
Dharamshi, Neufeld, Gao, Witten, and Bien (2025) Thinning a Wishart random matrix. arXiv:2502.09957
Kong, Ancell, Witten, and Zweifel (2024) Valence and salience encoding in the central amygdala. <i>eLife</i> 13: RP101980.
Dharamshi, Neufeld, Gao, Bien, and Witten (2025) Decomposing Gaussians with unknown covariance. To appear in <i>Biometrika</i>.
Perry, Xu, McGough, and Witten (2024) Infer-and-widen versus split-and-condition: two tales of selective inference. arXiv:2408.06323
Neufeld, Dharamshi, Gao, Witten, and Bien (2025) Discussion of "Data fission: splitting a single data point". <i>Journal of the American Statistical Association</i> 120(549): 151-157.
Perry, Panigrahi, Bien, and Witten (2025) Inference on the proportion of variance explained in principal component analysis. To appear in <I>J. American Statistical Association</i>.
James, Witten, Hastie, Tibshirani, and Taylor (2023) An Introduction to Statistical Learning, with Applications in Python. Springer Series in Statistics. 607 pages.
James, Witten, Hastie, and Tibshirani (2021) An Introduction to Statistical Learning, with Application in R, 2nd Edition. Springer Series in Statistics. 622 pages.
Motwani and Witten (2023) Re-visiting inference after prediction. <i>Journal of Machine Learning Research</i> 24(394): 1−18.
Zdeblick, Shea-Brown, Witten, and Buice (2023) Modeling functional cell types in spike train data. <i>PLoS Computational Biology</i> 19(10): e1011509.
Neufeld, Popp, Gao, Battle, and Witten (2023) Negative binomial count splitting for single-cell RNA sequencing data. arXiv:2307.12985
Dharamshi, Neufeld, Motwani, Gao, Witten, and Bien (2025) Generalized data thinning using sufficient statistics. <i>Journal of the American Statistical Association</I> 120(549): 511-523.
Neufeld, Dharamshi, Gao, and Witten (2024) Data thinning for convolution-closed distributions. <i>Journal of Machine Learning Research</I> 25(57): 1−35.
Saha, Witten, and Bien (2025) Inferring independent sets of Gaussian variables after thresholding correlations. <i>Journal of the American Statistical Association</I> 120(549): 370-381.
Chen and Witten (2023) Selective inference for k-means clustering. <i>Journal of Machine Learning Research</i> 24(152): 1-41.
Neufeld, Gao, Popp, Battle, and Witten (2024) Inference after latent variable estimation for single-cell RNA sequencing data. <i>Biostatistics</I> 25(1): 270-287.
Tan, Sun, and Witten (2023) Sparse reduced rank Huber regression in high dimensions. <i>Journal of the American Statistical Association</i> 118(544): 2383–2393.
