G. Schiebinger*, J. Shu*, M. Tabaka*, B. Cleary*, V. Subramanian, J. Gould, A. Solomon, S. Liu, S. Lin, P. Berube, L. Lee, J. Chen, J. Brumbaugh, P. Rigollet, K. Hochedlinger, R. Jaenisch, A. Regev and E. Lander (2019). Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming. Cell. (bioRxiv version posted 2017).
N. Boyd, G. Schiebinger and B. Recht (2017). The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems. SIAM Journal on Optimization. Our code is available here.
G. Schiebinger, E. Robeva and B. Recht (2017). Superresolution without Separation. Information and Inference, Oxford University Press.
G. Schiebinger, M. J. Wainwright and B. Yu (2015). The Geometry of Kernelized Spectral Clustering. Annals of Statistics. vol. 43, no. 2, pages 819-846.
A. Guntuboyina, S. Saha and G. Schiebinger (2014). Sharp Inequalities for f-divergences. IEEE Transactions on Information Theory. vol. 60, pages 104-121.
L. A. Warren, D. J. Rossi, G. Schiebinger, I. L. Weissman, S. K. Kim and S. R. Quake (2007). Transcriptional instability is not a universal attribute of aging. Aging Cell. vol. 6, pages 775-782.
A. Forrow, J.C. Hutter, M. Nitzan, P. Rigollet, G. Schiebinger, and J. Weed. Statistical Optimal Transport via Factored Couplings. AISTATS 2019.
M.E. Shiffman, W. Stephenson, G. Schiebinger, T. Campbell, J. Huggins, A. Regev, and T. Broderick. (2017). Probabilistic reconstruction of cellular differentiation trees from single-cell RNA-seq data. NIPS Workshop on Bayesian Computation.
A short version of Superresolution without Separation appeared in CAMSAP 2015. (full version published in journal, see above).
A short version of The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems appeared in CAMSAP 2015. (full version published in journal, see above).