Preprints

A. Pensia, V. Jog, and P. Loh. Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities.  Submitted, 2022.

M. Avella-Medina, C. Bradshaw, and P. Loh. Differentially private inference via noisy optimization.  Submitted, 2021.

A. Pensia, V. Jog, and P. Loh. Robust regression with covariate filtering: Heavy tails and adversarial contamination.  Submitted, 2021.

A. Pensia, A. Tovar Lopez, V. Jog, and P. Loh. Analyzing generalization error of learning algorithms: From information theory to optimal transport.  Submitted, 2020.

D. Wang, H. Fu, and P. Loh. Boosting algorithms for estimating optimal individualized treatment rules. 2020.

D. Wang and P. Loh. Adaptive estimation and statistical inference for high-dimensional graph-based linear models. 2020.

J. Khim and P. Loh. Adversarial risk bounds for binary classification via function transformation. 2018.

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

conference papers

J. Khim, V. Jog, and P. Loh. Adversarial influence maximization. Proceedings of the ISIT Conference, July 2019.

A. Pensia, V. Jog, and P. Loh. Mean estimation for entangled single-sample distributions. Proceedings of the ISIT Conference, July 2019.

S. Rajput, Z. Feng,  Z. Charles, P. Loh, and D. Papailiopoulos. Does data augmentation lead to positive margin? Proceedings of the ICML Conference, June 2019.

M. Pydi, V. Jog, and P. Loh. Graph-based ascent algorithms for function maximization. Proceedings of the 56rd Annual Allerton Conference on Communication, Control, and Computing, October 2018.

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

A. Pensia, V. Jog, and P. Loh. Generalization error bounds for noisy, iterative algorithms. Proceedings of the ISIT Conference, Vail, CO, June 2018.

A. Wibisono, V. Jog, and P. Loh. Information and estimation in Fokker-Planck channels. Proceedings of 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. Proceedings of 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. Proceedings of the ISIT Conference, Boston, MA, July 2012.

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

journal papers

V. Jog and P. Loh. Teaching and learning in uncertainty. IEEE Transactions on Information Theory 67(1): 598-615, 2021.

M. Kim, E. Ou, P. Loh, T. Allen, R. Agasie, and K. Liu. RNN-based online anomaly detection in nuclear reactors for highly imbalanced datasets with uncertainty. Nuclear Engineering and Design 364: 110699, 2020.

A. Pensia, V. Jog, and P. Loh. Extracting robust and accurate features via a robust information bottleneck. IEEE Journal on Selected Areas in Information Theory 1-14, 2020.

J. Khim and P. Loh. Permutation tests for infection graphs. Journal of the American Statistical Association 1-13, 2020.

M. Xu, V. Jog, and P. Loh. Optimal rates for community estimation in the weighted stochastic block model. Annals of Statistics 48(1): 183-204, 2020.

W. Yan, P. Loh, C. Li, Y. Huang, and L. Yang. Conquering the worst case of infections in networks. IEEE Access 8(1): 2835-2846, 2019.

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. Frontiers in Neuroscience 12: 861, 2018.

P. Loh and X. Tan. High-dimensional robust precision matrix estimation: Cellwise corruption under epsilon-contamination. Electronic Journal of Statistics 12(1): 1429-1467, 2018.

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. Bühlmann. 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.