Po-Ling Loh

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 60

Top Topics

Generalization Error

Hypothesis Test

Robust Regression

Top Venues

IEEE Trans. Inf. Theory

Publications

Ankit Pensia, Varun S. Jog, Po-Ling Loh
Communication-Constrained Hypothesis Testing: Optimality, Robustness, and Reverse Data Processing Inequalities. IEEE Trans. Inf. Theory 70 (1) (2024)
Nikolija Bojkovic, Po-Ling Loh
Differentially Private Synthetic Data with Private Density Estimation. CoRR (2024)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
The Sample Complexity of Simple Binary Hypothesis Testing. CoRR (2024)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
The Sample Complexity of Simple Binary Hypothesis Testing. COLT (2024)
Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar
On Differentially Private U Statistics. CoRR (2024)
Eirini Ioannou, Muni Sreenivas Pydi, Po-Ling Loh
Robust empirical risk minimization via Newton's method. CoRR (2023)
Amir-Reza Asadi, Po-Ling Loh
On the Gibbs Exponential Mechanism and Private Synthetic Data Generation. ISIT (2023)
Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. COLT (2023)
Ankit Pensia, Amir R. Asadi, Varun S. Jog, Po-Ling Loh
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. CoRR (2023)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities. CoRR (2022)
Xiaomin Zhang, Xucheng Zhang, Po-Ling Loh, Yingyu Liang
On the identifiability of mixtures of ranking models. CoRR (2022)
Ankit Pensia, Po-Ling Loh, Varun S. Jog
Simple Binary Hypothesis Testing under Communication Constraints. ISIT (2022)
Zheng Liu, Jinnian Zhang, Varun S. Jog, Po-Ling Loh, Alan B. McMillan
Robustifying Deep Networks for Medical Image Segmentation. J. Digit. Imaging 34 (5) (2021)
Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh
Provable training set debugging for linear regression. Mach. Learn. 110 (10) (2021)
Zheng Liu, Po-Ling Loh
Robust W-GAN-Based Estimation Under Wasserstein Contamination. CoRR (2021)
Marco Avella-Medina, Casey Bradshaw, Po-Ling Loh
Differentially private inference via noisy optimization. CoRR (2021)
Varun S. Jog, Po-Ling Loh
Teaching and Learning in Uncertainty. IEEE Trans. Inf. Theory 67 (1) (2021)
Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu
Editorial. IEEE J. Sel. Areas Inf. Theory 1 (3) (2020)
Wen Yan, Po-Ling Loh, Chunguo Li, Yongming Huang, Luxi Yang
Conquering the Worst Case of Infections in Networks. IEEE Access 8 (2020)
Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh
Theory of Machine Learning Debugging via M-estimation. CoRR (2020)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
Extracting Robust and Accurate Features via a Robust Information Bottleneck. IEEE J. Sel. Areas Inf. Theory 1 (1) (2020)
Duzhe Wang, Haoda Fu, Po-Ling Loh
Boosting Algorithms for Estimating Optimal Individualized Treatment Rules. CoRR (2020)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
Robust regression with covariate filtering: Heavy tails and adversarial contamination. CoRR (2020)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
Mean estimation for entangled single-sample distributions. ISIT (2019)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
Extracting robust and accurate features via a robust information bottleneck. CoRR (2019)
Shashank Rajput, Zhili Feng, Zachary B. Charles, Po-Ling Loh, Dimitris S. Papailiopoulos
Does Data Augmentation Lead to Positive Margin? CoRR (2019)
Zheng Liu, Jinnian Zhang, Varun S. Jog, Po-Ling Loh, Alan B. McMillan
Robustifying deep networks for image segmentation. CoRR (2019)
Justin Khim, Varun S. Jog, Po-Ling Loh
Adversarial Influence Maximization. ISIT (2019)
Shashank Rajput, Zhili Feng, Zachary B. Charles, Po-Ling Loh, Dimitris S. Papailiopoulos
Does Data Augmentation Lead to Positive Margin? ICML (2019)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
Estimating location parameters in entangled single-sample distributions. CoRR (2019)
Karsten M. Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen
Introduction to the special issue for the ECML PKDD 2019 journal track. Mach. Learn. 108 (8-9) (2019)
Karsten M. Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen
Introduction to the special issue for the ECML PKDD 2019 journal track. Data Min. Knowl. Discov. 33 (5) (2019)
Zhili Feng, Po-Ling Loh
Online learning with graph-structured feedback against adaptive adversaries. CoRR (2018)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
Generalization Error Bounds for Noisy, Iterative Algorithms. ISIT (2018)
Justin Khim, Po-Ling Loh
Adversarial Risk Bounds for Binary Classification via Function Transformation. CoRR (2018)
Zhili Feng, Po-Ling Loh
Online Learning with Graph-Structured Feedback against Adaptive Adversaries. ISIT (2018)
Ankit Pensia, Varun S. Jog, Po-Ling Loh
Generalization Error Bounds for Noisy, Iterative Algorithms. CoRR (2018)
Muni Sreenivas Pydi, Varun S. Jog, Po-Ling Loh
Graph-Based Ascent Algorithms for Function Maximization. CoRR (2018)
Po-Ling Loh
Scale calibration for high-dimensional robust regression. CoRR (2018)
Muni Sreenivas Pydi, Varun S. Jog, Po-Ling Loh
Graph-Based Ascent Algorithms for Function Maximization. Allerton (2018)
Varun S. Jog, Po-Ling Loh
Persistence of centrality in random growing trees. Random Struct. Algorithms 52 (1) (2018)
Po-Ling Loh, Arya Mazumdar, Dimitris S. Papailiopoulos, Rüdiger L. Urbanke
Coding Theory for Inference, Learning and Optimization (Dagstuhl Seminar 18112). Dagstuhl Reports 8 (3) (2018)
Justin Khim, Po-Ling Loh
Confidence Sets for the Source of a Diffusion in Regular Trees. IEEE Trans. Netw. Sci. Eng. 4 (1) (2017)
Andre Wibisono, Varun S. Jog, Po-Ling Loh
Information and estimation in Fokker-Planck channels. ISIT (2017)
Andre Wibisono, Varun S. Jog, Po-Ling Loh
Information and estimation in Fokker-Planck channels. CoRR (2017)
Justin Khim, Po-Ling Loh
Permutation Tests for Infection Graphs. CoRR (2017)
Po-Ling Loh
On Lower Bounds for Statistical Learning Theory. Entropy 19 (11) (2017)
Varun S. Jog, Po-Ling Loh
Analysis of Centrality in Sublinear Preferential Attachment Trees via the Crump-Mode-Jagers Branching Process. IEEE Trans. Netw. Sci. Eng. 4 (1) (2017)
Justin T. Khim, Varun S. Jog, Po-Ling Loh
Computing and maximizing influence in linear threshold and triggering models. NIPS (2016)
Justin Khim, Varun S. Jog, Po-Ling Loh
Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis. CoRR (2016)
Miao Cheng, Anand Sriramulu, Sudarshan Muralidhar, Boon Thau Loo, Laura Huang, Po-Ling Loh
Collection, exploration and analysis of crowdfunding social networks. ExploreDB@SIGMOD/PODS (2016)
Varun S. Jog, Po-Ling Loh
Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process. CoRR (2016)
Po-Ling Loh, Martin J. Wainwright
Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima. J. Mach. Learn. Res. 16 (2015)
Varun S. Jog, Po-Ling Loh
Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence. CoRR (2015)
Varun S. Jog, Po-Ling Loh
On model misspecification and KL separation for Gaussian graphical models. CoRR (2015)
Po-Ling Loh
Statistical consistency and asymptotic normality for high-dimensional robust M-estimators. CoRR (2015)
Varun S. Jog, Po-Ling Loh
Recovering communities in weighted stochastic block models. Allerton (2015)
Varun S. Jog, Po-Ling Loh
Persistence of centrality in random growing trees. CoRR (2015)
Justin Khim, Po-Ling Loh
Confidence Sets for the Source of a Diffusion in Regular Trees. CoRR (2015)
Varun S. Jog, Po-Ling Loh
On model misspecification and KL separation for Gaussian graphical models. ISIT (2015)
Po-Ling Loh, Peter Bühlmann
High-dimensional learning of linear causal networks via inverse covariance estimation. J. Mach. Learn. Res. 15 (1) (2014)
Po-Ling Loh, Andre Wibisono
Concavity of reweighted Kikuchi approximation. NIPS (2014)
Po-Ling Loh, Martin J. Wainwright
Support recovery without incoherence: A case for nonconvex regularization. CoRR (2014)
Po-Ling Loh, Martin J. Wainwright
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima CoRR (2013)
Po-Ling Loh, Sebastian Nowozin
Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates. ALT (2013)
Po-Ling Loh, Martin J. Wainwright
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima. NIPS (2013)
Po-Ling Loh, Martin J. Wainwright
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. NIPS (2012)
Po-Ling Loh, Martin J. Wainwright
Corrupted and missing predictors: Minimax bounds for high-dimensional linear regression. ISIT (2012)
Hongchao Zhou, Po-Ling Loh, Jehoshua Bruck
The Synthesis and Analysis of Stochastic Switching Circuits CoRR (2012)
Po-Ling Loh, Martin J. Wainwright
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity CoRR (2011)
Po-Ling Loh, Martin J. Wainwright
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. NIPS (2011)
Po-Ling Loh, Hongchao Zhou, Jehoshua Bruck
The robustness of stochastic switching networks. ISIT (2009)