The robustness of stochastic switching networks. - Po-Ling Loh; Hongchao Zhou; Jehoshua Bruck (ISIT, 2009)

High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. - Po-Ling Loh; Martin J. Wainwright (NIPS, 2011)

Corrupted and missing predictors: Minimax bounds for high-dimensional linear regression. - Po-Ling Loh; Martin J. Wainwright (ISIT, 2012)

Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. - Po-Ling Loh; Martin J. Wainwright (NIPS, 2012)

The Synthesis and Analysis of Stochastic Switching Circuits - Hongchao Zhou; Po-Ling Loh; Jehoshua Bruck (CoRR, 2012)

Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates. - Po-Ling Loh; Sebastian Nowozin (ALT, 2013)

Concavity of reweighted Kikuchi approximation. - Po-Ling Loh; Andre Wibisono (NIPS, 2014)

Support recovery without incoherence: A case for nonconvex regularization. - Po-Ling Loh; Martin J. Wainwright (CoRR, 2014)

High-dimensional learning of linear causal networks via inverse covariance estimation. - Po-Ling Loh; Peter Bühlmann (J. Mach. Learn. Res., 2014)

High-dimensional statistics with systematically corrupted data. - Po-Ling Loh (2014)

Recovering communities in weighted stochastic block models. - Varun S. Jog; Po-Ling Loh (Allerton, 2015)

On model misspecification and KL separation for Gaussian graphical models. - Varun S. Jog; Po-Ling Loh (ISIT, 2015)

Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence. - Varun S. Jog; Po-Ling Loh (CoRR, 2015)

Statistical consistency and asymptotic normality for high-dimensional robust M-estimators. - Po-Ling Loh (CoRR, 2015)

Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima. - Po-Ling Loh; Martin J. Wainwright (J. Mach. Learn. Res., 2015)

Computing and maximizing influence in linear threshold and triggering models. - Justin T. Khim; Varun S. Jog; Po-Ling Loh (NIPS, 2016)

Collection, exploration and analysis of crowdfunding social networks. - Miao Cheng; Anand Sriramulu; Sudarshan Muralidhar; Boon Thau Loo; Laura Huang; Po-Ling Loh (ExploreDB@SIGMOD/PODS, 2016)

Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process. - Varun S. Jog; Po-Ling Loh (CoRR, 2016)

Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis. - Justin Khim; Varun S. Jog; Po-Ling Loh (CoRR, 2016)

Information and estimation in Fokker-Planck channels. - Andre Wibisono; Varun S. Jog; Po-Ling Loh (ISIT, 2017)

Permutation Tests for Infection Graphs. - Justin Khim; Po-Ling Loh (CoRR, 2017)

On Lower Bounds for Statistical Learning Theory. - Po-Ling Loh (Entropy, 2017)

Analysis of Centrality in Sublinear Preferential Attachment Trees via the Crump-Mode-Jagers Branching Process. - Varun S. Jog; Po-Ling Loh (IEEE Trans. Netw. Sci. Eng., 2017)

Confidence Sets for the Source of a Diffusion in Regular Trees. - Justin Khim; Po-Ling Loh (IEEE Trans. Netw. Sci. Eng., 2017)

Graph-Based Ascent Algorithms for Function Maximization. - Muni Sreenivas Pydi; Varun S. Jog; Po-Ling Loh (Allerton, 2018)

Online Learning with Graph-Structured Feedback against Adaptive Adversaries. - Zhili Feng; Po-Ling Loh (ISIT, 2018)

Generalization Error Bounds for Noisy, Iterative Algorithms. - Ankit Pensia; Varun S. Jog; Po-Ling Loh (ISIT, 2018)

Adversarial Risk Bounds for Binary Classification via Function Transformation. - Justin Khim; Po-Ling Loh (CoRR, 2018)

Scale calibration for high-dimensional robust regression. - Po-Ling Loh (CoRR, 2018)

Coding Theory for Inference, Learning and Optimization (Dagstuhl Seminar 18112). - Po-Ling Loh; Arya Mazumdar; Dimitris S. Papailiopoulos; Rüdiger L. Urbanke (Dagstuhl Reports, 2018)

Persistence of centrality in random growing trees. - Varun S. Jog; Po-Ling Loh (Random Struct. Algorithms, 2018)

Does Data Augmentation Lead to Positive Margin? - Shashank Rajput; Zhili Feng; Zachary Charles; Po-Ling Loh; Dimitris S. Papailiopoulos (ICML, 2019)

Adversarial Influence Maximization. - Justin Khim; Varun S. Jog; Po-Ling Loh (ISIT, 2019)

Mean estimation for entangled single-sample distributions. - Ankit Pensia; Varun S. Jog; Po-Ling Loh (ISIT, 2019)

Estimating location parameters in entangled single-sample distributions. - Ankit Pensia; Varun S. Jog; Po-Ling Loh (CoRR, 2019)

Robustifying deep networks for image segmentation. - Zheng Liu; Jinnian Zhang; Varun S. Jog; Po-Ling Loh; Alan B. McMillan (CoRR, 2019)

Introduction to the special issue for the ECML PKDD 2019 journal track. - Karsten M. Borgwardt; Po-Ling Loh; Evimaria Terzi; Antti Ukkonen (Mach. Learn., 2019)

Conquering the Worst Case of Infections in Networks. - Wen Yan; Po-Ling Loh; Chunguo Li; Yongming Huang 001; Luxi Yang (IEEE Access, 2020)

Boosting Algorithms for Estimating Optimal Individualized Treatment Rules. - Duzhe Wang; Haoda Fu; Po-Ling Loh (CoRR, 2020)

Theory of Machine Learning Debugging via M-estimation. - Xiaomin Zhang; Xiaojin Zhu 001; Po-Ling Loh (CoRR, 2020)

Robust regression with covariate filtering: Heavy tails and adversarial contamination. - Ankit Pensia; Varun S. Jog; Po-Ling Loh (CoRR, 2020)

Extracting Robust and Accurate Features via a Robust Information Bottleneck. - Ankit Pensia; Varun S. Jog; Po-Ling Loh (IEEE J. Sel. Areas Inf. Theory, 2020)

Editorial. - Devavrat Shah; Guy Bresler; John C. Duchi; Po-Ling Loh; Yihong Wu 001; Christina Lee Yu (IEEE J. Sel. Areas Inf. Theory, 2020)

Robust W-GAN-Based Estimation Under Wasserstein Contamination. - Zheng Liu; Po-Ling Loh (CoRR, 2021)

Differentially private inference via noisy optimization. - Marco Avella-Medina; Casey Bradshaw; Po-Ling Loh (CoRR, 2021)

Robustifying Deep Networks for Medical Image Segmentation. - Zheng Liu; Jinnian Zhang; Varun S. Jog; Po-Ling Loh; Alan B. McMillan (J. Digit. Imaging, 2021)

Provable training set debugging for linear regression. - Xiaomin Zhang; Xiaojin Zhu 001; Po-Ling Loh (Mach. Learn., 2021)

Teaching and Learning in Uncertainty. - Varun S. Jog; Po-Ling Loh (IEEE Trans. Inf. Theory, 2021)

Simple Binary Hypothesis Testing under Communication Constraints. - Ankit Pensia; Po-Ling Loh; Varun S. Jog (ISIT, 2022)

On the identifiability of mixtures of ranking models. - Xiaomin Zhang; Xucheng Zhang; Po-Ling Loh; Yingyu Liang (CoRR, 2022)

On the Gibbs Exponential Mechanism and Private Synthetic Data Generation. - Amir-Reza Asadi; Po-Ling Loh (ISIT, 2023)

Robust empirical risk minimization via Newton's method. - Eirini Ioannou; Muni Sreenivas Pydi; Po-Ling Loh (CoRR, 2023)

The Sample Complexity of Simple Binary Hypothesis Testing. - Ankit Pensia; Varun S. Jog; Po-Ling Loh (COLT, 2024)

Differentially Private Synthetic Data with Private Density Estimation. - Nikolija Bojkovic; Po-Ling Loh (ISIT, 2024)

On Differentially Private U Statistics. - Kamalika Chaudhuri; Po-Ling Loh; Shourya Pandey; Purnamrita Sarkar (NeurIPS, 2024)

Communication-Constrained Hypothesis Testing: Optimality, Robustness, and Reverse Data Processing Inequalities. - Ankit Pensia; Varun S. Jog; Po-Ling Loh (IEEE Trans. Inf. Theory, 2024)

Simple Binary Hypothesis Testing Under Local Differential Privacy and Communication Constraints. - Ankit Pensia; Amir-Reza Asadi; Varun S. Jog; Po-Ling Loh (IEEE Trans. Inf. Theory, 2025)

On the Benefits of Accelerated Optimization in Robust and Private Estimation. - Laurentiu Andrei Marchis; Po-Ling Loh (CoRR, 2025)