Publications

books

under review

J. Khim and P. Loh, A theory of maximum likelihood for weighted infection graphs. Submitted, 2018.

M. Mazrooyisebdani, V. Nair, P. Loh, A. Remsik, B. Young, K. Dodd, T. Kang, J. William, and V. Prabhakaran. Evaluation of changes in motor network following BCI therapy based on graph theory analyses. Submitted, 2018.

M. Pydi, V. Jog, and P. Loh. Graph-based ascent algorithms for function maximization. Submitted, 2018.

X. Zhang, X. Zhang, X. Zhu, and P. Loh. Theoretical support of machine learning debugging via weighted M-estimation. Submitted, 2018.

M. Xu, V. Jog, and P. Loh. Optimal rates for community estimation in the weighted stochastic block model. Submitted, 2017.

J. Khim and P. Loh. Permutation tests for infection graphs. Submitted, 2017.

J. Khim, V. Jog, and P. Loh. Computationally efficient influence maximization in stochastic and adversarial models: Algorithms and analysis. Submitted, 2016.


conference papers

Z. Feng and P. Loh. Online learning with graph-structured feedback against adaptive adversaries. To be presented at the ISIT Conference, Vail, CO, June 2018.

A. Pensia, V. Jog, and P. Loh. Generalization error bounds for noisy, iterative algorithms. To be presented at the ISIT Conference, Vail, CO, June 2018.

A. Wibisono, V. Jog, and P. Loh. Information and estimation in Fokker-Planck channels. Presented at the ISIT Conference, Aachen, Germany, July 2017.

J. Khim, V. Jog, and P. Loh. Computing and maximizing influence in linear threshold and triggering models. Proceedings of the NIPS Conference, Barcelona, Spain, December 2016.

M. Cheng, A. Sriramulu, S. Muralidhar, B. Loo, L. Huang, and P. Loh. Collection, exploration and analysis of crowdfunding social networks. Proceedings of the Third International Workshop on Exploratory Search in Databases and the Web (ExploreDB), June 2016.

V. Jog and P. Loh. Recovering communities in weighted stochastic block models. Proceedings of the 53rd Annual Allerton Conference on Communication, Control, and Computing, October 2015.

V. Jog and P. Loh. On model misspecification and KL separation for Gaussian graphical models. Presented at the ISIT Conference, Hong Kong, June 2015.

P. Loh and A. Wibisono. Concavity of reweighted Kikuchi approximation. Proceedings of the NIPS Conference, Montreal, Canada, December 2014.

P. Loh and S. Nowozin. Faster Hoeffding racing: Bernstein races via jackknife estimates. Proceedings of the ALT Conference, Singapore, October 2013.

P. Loh and M.J. Wainwright. Corrupted and missing predictors: Minimax bounds for high-dimensional linear regression. Presented at the ISIT Conference, Boston, MA, July 2012.

P. Loh, H. Zhou, and J. Bruck. The robustness of stochastic switching networks. Presented at the ISIT Conference, Seoul, Korea, July 2009.


journal papers

P. Loh and X. Tan. High-dimensional robust precision matrix estimation: Cellwise corruption under epsilon-contamination. To appear in Electronic Journal of Statistics.

J. Ko, S. Baldassano, P. Loh, K. Kording, B. Litt, and D. Issadore. Machine learning to detect signatures of disease in liquid biopsies -- A user's guide. Lab on a Chip 18(3): 395-405, 2018.

V. Jog and P. Loh. Persistence of centrality in random growing trees. Random Structures and Algorithms 52(1): 136-157, 2018.

P. Loh. On lower bounds for statistical learning theory. Entropy 19(11), 617, 2017.

P.  Loh and M. J. Wainwright. Support recovery without incoherence: A case for nonconvex regularization. Annals of Statistics 45(6): 2455-2482, 2017.

P. Loh. Statistical consistency and asymptotic normality for high-dimensional robust M-estimators. Annals of Statistics 45(2): 866-896, 2017.

V. Jog and P. Loh. Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process.  IEEE Transactions on Network Science and Engineering 4(1): 1-12, 2016.

J. Khim and P. Loh. Confidence sets for the source of a diffusion in regular trees. IEEE Transactions on Network Science and Engineering 4(1): 27-40, 2016.

P. Loh and P. B├╝hlmann. High-dimensional learning of linear causal networks via inverse covariance estimation. Journal of Machine Learning Research 15 (2014) 3065--3105.

P. Loh and M.J. Wainwright. Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima. Journal of Machine Learning Research 16 (2015) 559--616. Spotlight presentation at the NIPS Conference, Lake Tahoe, NV, December 2013.

P. Loh and M.J. Wainwright. Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. Annals of Statistics, 41(6):3022--3049, 2013. Presented at the NIPS Conference, Lake Tahoe, NV, December 2012. Winner of best student paper award.

P. Loh and M.J. Wainwright. High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. Annals of Statistics, 40(3):1637--1664,  2012. Presented at the NIPS Conference, Granada, Spain, December 2011.