Loh, Po-Ling

16 publications

ALT 2025 Algorithmic Learning Theory 2025: Preface Gautam Kamath, Po-Ling Loh

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

COLT 2024 The Sample Complexity of Simple Binary Hypothesis Testing Ankit Pensia, Varun Jog, Po-Ling Loh

COLT 2023 Simple Binary Hypothesis Testing Under Local Differential Privacy and Communication Constraints Ankit Pensia, Amir Reza Asadi, Varun Jog, Po-Ling Loh

COLT 2022 Conference on Learning Theory 2022: Preface Po-Ling Loh, Maxim Raginsky

MLJ 2021 Provable Training Set Debugging for Linear Regression Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh

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

MLJ 2019 Introduction to the Special Issue for the ECML PKDD 2019 Journal Track Karsten M. Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen

NeurIPS 2016 Computing and Maximizing Influence in Linear Threshold and Triggering Models Justin T Khim, Varun Jog, Po-Ling Loh

JMLR 2015 Regularized M-Estimators with Nonconvexity: Statistical and Algorithmic Theory for Local Optima Po-Ling Loh, Martin J. Wainwright

NeurIPS 2014 Concavity of Reweighted Kikuchi Approximation Po-Ling Loh, Andre Wibisono

JMLR 2014 High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation Po-Ling Loh, Peter Bühlmann

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

NeurIPS 2013 Regularized M-Estimators with Nonconvexity: Statistical and Algorithmic Theory for Local Optima Po-Ling Loh, Martin J. Wainwright

NeurIPS 2012 Structure Estimation for Discrete Graphical Models: Generalized Covariance Matrices and Their Inverses Po-ling Loh, Martin J. Wainwright

NeurIPS 2011 High-Dimensional Regression with Noisy and Missing Data: Provable Guarantees with Non-Convexity Po-ling Loh, Martin J. Wainwright