Po-Ling Loh

According to our database, Po-Ling Loh authored at least 57 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Communication-Constrained Hypothesis Testing: Optimality, Robustness, and Reverse Data Processing Inequalities.

Ankit Pensia, Varun S. Jog, Po-Ling Loh

IEEE Trans. Inf. Theory, January, 2024

On Differentially Private U Statistics.

Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar

CoRR, 2024

Differentially Private Synthetic Data with Private Density Estimation.

Nikolija Bojkovic, Po-Ling Loh

Proceedings of the IEEE International Symposium on Information Theory, 2024

The Sample Complexity of Simple Binary Hypothesis Testing.

Ankit Pensia, Varun S. Jog, Po-Ling Loh

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Robust empirical risk minimization via Newton's method.

Eirini Ioannou, Muni Sreenivas Pydi, Po-Ling Loh

CoRR, 2023

On the Gibbs Exponential Mechanism and Private Synthetic Data Generation.

Amir-Reza Asadi, Po-Ling Loh

Proceedings of the IEEE International Symposium on Information Theory, 2023

Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints.

Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

On the identifiability of mixtures of ranking models.

Xiaomin Zhang, Xucheng Zhang, Po-Ling Loh, Yingyu Liang

CoRR, 2022

Simple Binary Hypothesis Testing under Communication Constraints.

Ankit Pensia, Po-Ling Loh, Varun S. Jog

Proceedings of the IEEE International Symposium on Information Theory, 2022

2021

Teaching and Learning in Uncertainty.

Varun S. Jog, Po-Ling Loh

IEEE Trans. Inf. Theory, 2021

Provable training set debugging for linear regression.

Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh

Mach. Learn., 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

Differentially private inference via noisy optimization.

Marco Avella-Medina, Casey Bradshaw, Po-Ling Loh

CoRR, 2021

Robust W-GAN-Based Estimation Under Wasserstein Contamination.

Zheng Liu, Po-Ling Loh

CoRR, 2021

2020

Editorial.

Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu

IEEE J. Sel. Areas Inf. Theory, 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

Robust regression with covariate filtering: Heavy tails and adversarial contamination.

Ankit Pensia, Varun S. Jog, Po-Ling Loh

CoRR, 2020

Theory of Machine Learning Debugging via M-estimation.

Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh

CoRR, 2020

Boosting Algorithms for Estimating Optimal Individualized Treatment Rules.

Duzhe Wang, Haoda Fu, Po-Ling Loh

CoRR, 2020

Conquering the Worst Case of Infections in Networks.

Wen Yan, Po-Ling Loh, Chunguo Li, Yongming Huang, Luxi Yang

IEEE Access, 2020

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

Robustifying deep networks for image segmentation.

Zheng Liu, Jinnian Zhang, Varun S. Jog, Po-Ling Loh, Alan B. McMillan

CoRR, 2019

Estimating location parameters in entangled single-sample distributions.

Ankit Pensia, Varun S. Jog, Po-Ling Loh

CoRR, 2019

Mean estimation for entangled single-sample distributions.

Ankit Pensia, Varun S. Jog, Po-Ling Loh

Proceedings of the IEEE International Symposium on Information Theory, 2019

Adversarial Influence Maximization.

Justin Khim, Varun S. Jog, Po-Ling Loh

Proceedings of the IEEE International Symposium on Information Theory, 2019

Does Data Augmentation Lead to Positive Margin?

Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Persistence of centrality in random growing trees.

Varun S. Jog, Po-Ling Loh

Random Struct. Algorithms, 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

Scale calibration for high-dimensional robust regression.

Po-Ling Loh

CoRR, 2018

Adversarial Risk Bounds for Binary Classification via Function Transformation.

Justin Khim, Po-Ling Loh

CoRR, 2018

Generalization Error Bounds for Noisy, Iterative Algorithms.

Ankit Pensia, Varun S. Jog, Po-Ling Loh

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Online Learning with Graph-Structured Feedback against Adaptive Adversaries.

Zhili Feng, Po-Ling Loh

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Graph-Based Ascent Algorithms for Function Maximization.

Muni Sreenivas Pydi, Varun S. Jog, Po-Ling Loh

Proceedings of the 56th Annual Allerton Conference on Communication, 2018

2017

Confidence Sets for the Source of a Diffusion in Regular Trees.

Justin Khim, Po-Ling Loh

IEEE Trans. Netw. Sci. Eng., 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

On Lower Bounds for Statistical Learning Theory.

Po-Ling Loh

Entropy, 2017

Permutation Tests for Infection Graphs.

Justin Khim, Po-Ling Loh

CoRR, 2017

Information and estimation in Fokker-Planck channels.

Andre Wibisono, Varun S. Jog, Po-Ling Loh

Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

2016

Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis.

Justin Khim, Varun S. Jog, Po-Ling Loh

CoRR, 2016

Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process.

Varun S. Jog, Po-Ling Loh

CoRR, 2016

Collection, exploration and analysis of crowdfunding social networks.

Miao Cheng, Anand Sriramulu, Sudarshan Muralidhar, Boon Thau Loo, Laura Huang, Po-Ling Loh

Proceedings of the Third International Workshop on Exploratory Search in Databases and the Web, 2016

Computing and maximizing influence in linear threshold and triggering models.

Justin T. Khim, Varun S. Jog, Po-Ling Loh

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima.

Po-Ling Loh, Martin J. Wainwright

J. Mach. Learn. Res., 2015

Statistical consistency and asymptotic normality for high-dimensional robust M-estimators.

Po-Ling Loh

CoRR, 2015

Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence.

Varun S. Jog, Po-Ling Loh

CoRR, 2015

On model misspecification and KL separation for Gaussian graphical models.

Varun S. Jog, Po-Ling Loh

Proceedings of the IEEE International Symposium on Information Theory, 2015

Recovering communities in weighted stochastic block models.

Varun S. Jog, Po-Ling Loh

Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

2014

High-dimensional statistics with systematically corrupted data.

Po-Ling Loh

PhD thesis, 2014

High-dimensional learning of linear causal networks via inverse covariance estimation.

Po-Ling Loh, Peter Bühlmann

J. Mach. Learn. Res., 2014

Support recovery without incoherence: A case for nonconvex regularization.

Po-Ling Loh, Martin J. Wainwright

CoRR, 2014

Concavity of reweighted Kikuchi approximation.

Po-Ling Loh, Andre Wibisono

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates.

Po-Ling Loh, Sebastian Nowozin

Proceedings of the Algorithmic Learning Theory - 24th International Conference, 2013

2012

The Synthesis and Analysis of Stochastic Switching Circuits

Hongchao Zhou, Po-Ling Loh, Jehoshua Bruck

CoRR, 2012

Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses.

Po-Ling Loh, Martin J. Wainwright

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Corrupted and missing predictors: Minimax bounds for high-dimensional linear regression.

Po-Ling Loh, Martin J. Wainwright

Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

2011

High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity.

Po-Ling Loh, Martin J. Wainwright

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2009

The robustness of stochastic switching networks.

Po-Ling Loh, Hongchao Zhou, Jehoshua Bruck

Proceedings of the IEEE International Symposium on Information Theory, 2009