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2020 – today
- 2024
[j15]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): 389-414 (2024)- [c23]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
The Sample Complexity of Simple Binary Hypothesis Testing. COLT 2024: 4205-4206 - [c22]Nikolija Bojkovic, Po-Ling Loh:
Differentially Private Synthetic Data with Private Density Estimation. ISIT 2024: 599-604 - [i35]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
The Sample Complexity of Simple Binary Hypothesis Testing. CoRR abs/2403.16981 (2024) - [i34]Nikolija Bojkovic, Po-Ling Loh:
Differentially Private Synthetic Data with Private Density Estimation. CoRR abs/2405.04554 (2024) - [i33]Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar:
On Differentially Private U Statistics. CoRR abs/2407.04945 (2024) - 2023
- [c21]Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh:
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. COLT 2023: 3229-3230 - [c20]Amir-Reza Asadi, Po-Ling Loh:
On the Gibbs Exponential Mechanism and Private Synthetic Data Generation. ISIT 2023: 2213-2218 - [i32]Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh:
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. CoRR abs/2301.03566 (2023) - [i31]Eirini Ioannou, Muni Sreenivas Pydi, Po-Ling Loh:
Robust empirical risk minimization via Newton's method. CoRR abs/2301.13192 (2023) - 2022
- [c19]Ankit Pensia, Po-Ling Loh, Varun S. Jog:
Simple Binary Hypothesis Testing under Communication Constraints. ISIT 2022: 3297-3302 - [e1]Po-Ling Loh, Maxim Raginsky:
Conference on Learning Theory, 2-5 July 2022, London, UK. Proceedings of Machine Learning Research 178, PMLR 2022 [contents] - [i30]Xiaomin Zhang, Xucheng Zhang, Po-Ling Loh, Yingyu Liang:
On the identifiability of mixtures of ranking models. CoRR abs/2201.13132 (2022) - [i29]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities. CoRR abs/2206.02765 (2022) - 2021
- [j14]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): 1279-1293 (2021) - [j13]Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh:
Provable training set debugging for linear regression. Mach. Learn. 110(10): 2763-2834 (2021) - [j12]Varun S. Jog, Po-Ling Loh:
Teaching and Learning in Uncertainty. IEEE Trans. Inf. Theory 67(1): 598-615 (2021) - [i28]Zheng Liu, Po-Ling Loh:
Robust W-GAN-Based Estimation Under Wasserstein Contamination. CoRR abs/2101.07969 (2021) - [i27]Marco Avella-Medina, Casey Bradshaw, Po-Ling Loh:
Differentially private inference via noisy optimization. CoRR abs/2103.11003 (2021) - 2020
- [j11]Wen Yan, Po-Ling Loh, Chunguo Li, Yongming Huang, Luxi Yang:
Conquering the Worst Case of Infections in Networks. IEEE Access 8: 2835-2846 (2020) - [j10]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): 131-144 (2020) - [j9]Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu:
Editorial. IEEE J. Sel. Areas Inf. Theory 1(3): 612 (2020) - [i26]Duzhe Wang, Haoda Fu, Po-Ling Loh:
Boosting Algorithms for Estimating Optimal Individualized Treatment Rules. CoRR abs/2002.00079 (2020) - [i25]Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh:
Theory of Machine Learning Debugging via M-estimation. CoRR abs/2006.09009 (2020) - [i24]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
Robust regression with covariate filtering: Heavy tails and adversarial contamination. CoRR abs/2009.12976 (2020)
2010 – 2019
- 2019
- [j8]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): 1223-1224 (2019) - [j7]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): 1191-1192 (2019) - [c18]Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? ICML 2019: 5321-5330 - [c17]Justin Khim, Varun S. Jog, Po-Ling Loh:
Adversarial Influence Maximization. ISIT 2019: 1-5 - [c16]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
Mean estimation for entangled single-sample distributions. ISIT 2019: 3052-3056 - [i23]Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? CoRR abs/1905.03177 (2019) - [i22]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
Estimating location parameters in entangled single-sample distributions. CoRR abs/1907.03087 (2019) - [i21]Zheng Liu, Jinnian Zhang, Varun S. Jog, Po-Ling Loh, Alan B. McMillan:
Robustifying deep networks for image segmentation. CoRR abs/1908.00656 (2019) - [i20]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
Extracting robust and accurate features via a robust information bottleneck. CoRR abs/1910.06893 (2019) - 2018
- [j6]Varun S. Jog, Po-Ling Loh:
Persistence of centrality in random growing trees. Random Struct. Algorithms 52(1): 136-157 (2018) - [c15]Muni Sreenivas Pydi, Varun S. Jog, Po-Ling Loh:
Graph-Based Ascent Algorithms for Function Maximization. Allerton 2018: 1-8 - [c14]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
Generalization Error Bounds for Noisy, Iterative Algorithms. ISIT 2018: 546-550 - [c13]Zhili Feng, Po-Ling Loh:
Online Learning with Graph-Structured Feedback against Adaptive Adversaries. ISIT 2018: 931-935 - [i19]Ankit Pensia, Varun S. Jog, Po-Ling Loh:
Generalization Error Bounds for Noisy, Iterative Algorithms. CoRR abs/1801.04295 (2018) - [i18]Muni Sreenivas Pydi, Varun S. Jog, Po-Ling Loh:
Graph-Based Ascent Algorithms for Function Maximization. CoRR abs/1802.04475 (2018) - [i17]Zhili Feng, Po-Ling Loh:
Online learning with graph-structured feedback against adaptive adversaries. CoRR abs/1804.00335 (2018) - [i16]Justin Khim, Po-Ling Loh:
Adversarial Risk Bounds for Binary Classification via Function Transformation. CoRR abs/1810.09519 (2018) - [i15]Po-Ling Loh:
Scale calibration for high-dimensional robust regression. CoRR abs/1811.02096 (2018) - [i14]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): 60-73 (2018) - 2017
- [j5]Po-Ling Loh:
On Lower Bounds for Statistical Learning Theory. Entropy 19(11): 617 (2017) - [j4]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): 1-12 (2017) - [j3]Justin Khim, Po-Ling Loh:
Confidence Sets for the Source of a Diffusion in Regular Trees. IEEE Trans. Netw. Sci. Eng. 4(1): 27-40 (2017) - [c12]Andre Wibisono, Varun S. Jog, Po-Ling Loh:
Information and estimation in Fokker-Planck channels. ISIT 2017: 2673-2677 - [i13]Andre Wibisono, Varun S. Jog, Po-Ling Loh:
Information and estimation in Fokker-Planck channels. CoRR abs/1702.03656 (2017) - [i12]Justin Khim, Po-Ling Loh:
Permutation Tests for Infection Graphs. CoRR abs/1705.07997 (2017) - 2016
- [c11]Justin T. Khim, Varun S. Jog, Po-Ling Loh:
Computing and maximizing influence in linear threshold and triggering models. NIPS 2016: 4538-4546 - [c10]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: 25-30 - [i11]Varun S. Jog, Po-Ling Loh:
Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process. CoRR abs/1601.06448 (2016) - [i10]Justin Khim, Varun S. Jog, Po-Ling Loh:
Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis. CoRR abs/1611.00350 (2016) - 2015
- [j2]Po-Ling Loh, Martin J. Wainwright:
Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima. J. Mach. Learn. Res. 16: 559-616 (2015) - [c9]Varun S. Jog, Po-Ling Loh:
Recovering communities in weighted stochastic block models. Allerton 2015: 1308-1315 - [c8]Varun S. Jog, Po-Ling Loh:
On model misspecification and KL separation for Gaussian graphical models. ISIT 2015: 1174-1178 - [i9]Po-Ling Loh:
Statistical consistency and asymptotic normality for high-dimensional robust M-estimators. CoRR abs/1501.00312 (2015) - [i8]Varun S. Jog, Po-Ling Loh:
On model misspecification and KL separation for Gaussian graphical models. CoRR abs/1501.02320 (2015) - [i7]Varun S. Jog, Po-Ling Loh:
Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence. CoRR abs/1509.06418 (2015) - [i6]Justin Khim, Po-Ling Loh:
Confidence Sets for the Source of a Diffusion in Regular Trees. CoRR abs/1510.05461 (2015) - [i5]Varun S. Jog, Po-Ling Loh:
Persistence of centrality in random growing trees. CoRR abs/1511.01975 (2015) - 2014
- [b1]Po-Ling Loh:
High-dimensional statistics with systematically corrupted data. University of California, Berkeley, USA, 2014 - [j1]Po-Ling Loh, Peter Bühlmann:
High-dimensional learning of linear causal networks via inverse covariance estimation. J. Mach. Learn. Res. 15(1): 3065-3105 (2014) - [c7]Po-Ling Loh, Andre Wibisono:
Concavity of reweighted Kikuchi approximation. NIPS 2014: 3473-3481 - [i4]Po-Ling Loh, Martin J. Wainwright:
Support recovery without incoherence: A case for nonconvex regularization. CoRR abs/1412.5632 (2014) - 2013
- [c6]Po-Ling Loh, Sebastian Nowozin:
Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates. ALT 2013: 203-217 - [c5]Po-Ling Loh, Martin J. Wainwright:
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima. NIPS 2013: 476-484 - [i3]Po-Ling Loh, Martin J. Wainwright:
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima. CoRR abs/1305.2436 (2013) - 2012
- [c4]Po-Ling Loh, Martin J. Wainwright:
Corrupted and missing predictors: Minimax bounds for high-dimensional linear regression. ISIT 2012: 2601-2605 - [c3]Po-Ling Loh, Martin J. Wainwright:
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. NIPS 2012: 2096-2104 - [i2]Hongchao Zhou, Po-Ling Loh, Jehoshua Bruck:
The Synthesis and Analysis of Stochastic Switching Circuits. CoRR abs/1209.0715 (2012) - 2011
- [c2]Po-Ling Loh, Martin J. Wainwright:
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. NIPS 2011: 2726-2734 - [i1]Po-Ling Loh, Martin J. Wainwright:
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. CoRR abs/1109.3714 (2011)
2000 – 2009
- 2009
- [c1]Po-Ling Loh, Hongchao Zhou, Jehoshua Bruck:
The robustness of stochastic switching networks. ISIT 2009: 2066-2070
Coauthor Index
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