Po-Ling Loh - RankMe

79 papers A* 5B 13Journal 52Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	B	`ed.`	International Conference on Algorithmic Learning Theory, 24-27 February 2025, Politecnico di Milano, Milan, Italy. ALT Gautam Kamath, Po-Ling Loh
2025	J	`jnl`	On the Benefits of Accelerated Optimization in Robust and Private Estimation. CoRR Laurentiu Andrei Marchis, Po-Ling Loh
2025	J	`jnl`	Simple Binary Hypothesis Testing Under Local Differential Privacy and Communication Constraints. IEEE Trans. Inf. Theory Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh
2024	J	`jnl`	Communication-Constrained Hypothesis Testing: Optimality, Robustness, and Reverse Data Processing Inequalities. IEEE Trans. Inf. Theory Ankit Pensia, Varun S. Jog, Po-Ling Loh
2024	B	`conf`	Differentially Private Synthetic Data with Private Density Estimation. ISIT Nikolija Bojkovic, Po-Ling Loh
2024	J	`jnl`	Differentially Private Synthetic Data with Private Density Estimation. CoRR Nikolija Bojkovic, Po-Ling Loh
2024	A*	`conf`	On Differentially Private U Statistics. NeurIPS Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar
2024	J	`jnl`	On Differentially Private U Statistics. CoRR Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar
2024	A*	`conf`	The Sample Complexity of Simple Binary Hypothesis Testing. COLT Ankit Pensia, Varun S. Jog, Po-Ling Loh
2024	J	`jnl`	The Sample Complexity of Simple Binary Hypothesis Testing. CoRR Ankit Pensia, Varun S. Jog, Po-Ling Loh
2023	B	`conf`	On the Gibbs Exponential Mechanism and Private Synthetic Data Generation. ISIT Amir-Reza Asadi, Po-Ling Loh
2023	J	`jnl`	Robust empirical risk minimization via Newton's method. CoRR Eirini Ioannou, Muni Sreenivas Pydi, Po-Ling Loh
2023	A*	`conf`	Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. COLT Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh
2023	J	`jnl`	Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. CoRR Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh
2022	J	`jnl`	Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities. CoRR Ankit Pensia, Varun S. Jog, Po-Ling Loh
2022	A*	`ed.`	Conference on Learning Theory, 2-5 July 2022, London, UK. COLT Po-Ling Loh, Maxim Raginsky
2022	J	`jnl`	On the identifiability of mixtures of ranking models. CoRR Xiaomin Zhang, Xucheng Zhang, Po-Ling Loh, Yingyu Liang
2022	B	`conf`	Simple Binary Hypothesis Testing under Communication Constraints. ISIT Ankit Pensia, Po-Ling Loh, Varun S. Jog
2021	J	`jnl`	Differentially private inference via noisy optimization. CoRR Marco Avella-Medina, Casey Bradshaw, Po-Ling Loh
2021	J	`jnl`	Provable training set debugging for linear regression. Mach. Learn. Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh
2021	J	`jnl`	Robust W-GAN-Based Estimation Under Wasserstein Contamination. CoRR Zheng Liu, Po-Ling Loh
2021	J	`jnl`	Robustifying Deep Networks for Medical Image Segmentation. J. Digit. Imaging Zheng Liu, Jinnian Zhang, Varun S. Jog, Po-Ling Loh, Alan B. McMillan
2021	J	`jnl`	Teaching and Learning in Uncertainty. IEEE Trans. Inf. Theory Varun S. Jog, Po-Ling Loh
2020	J	`jnl`	Boosting Algorithms for Estimating Optimal Individualized Treatment Rules. CoRR Duzhe Wang, Haoda Fu, Po-Ling Loh
2020	J	`jnl`	Conquering the Worst Case of Infections in Networks. IEEE Access Wen Yan, Po-Ling Loh, Chunguo Li, Yongming Huang, Luxi Yang
2020	J	`jnl`	Editorial. IEEE J. Sel. Areas Inf. Theory Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu
2020	J	`jnl`	Extracting Robust and Accurate Features via a Robust Information Bottleneck. IEEE J. Sel. Areas Inf. Theory Ankit Pensia, Varun S. Jog, Po-Ling Loh
2020	J	`jnl`	Robust regression with covariate filtering: Heavy tails and adversarial contamination. CoRR Ankit Pensia, Varun S. Jog, Po-Ling Loh
2020	J	`jnl`	Theory of Machine Learning Debugging via M-estimation. CoRR Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh
2019	B	`conf`	Adversarial Influence Maximization. ISIT Justin Khim, Varun S. Jog, Po-Ling Loh
2019	A*	`conf`	Does Data Augmentation Lead to Positive Margin? ICML Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos
2019	J	`jnl`	Does Data Augmentation Lead to Positive Margin? CoRR Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos
2019	J	`jnl`	Estimating location parameters in entangled single-sample distributions. CoRR Ankit Pensia, Varun S. Jog, Po-Ling Loh
2019	J	`jnl`	Extracting robust and accurate features via a robust information bottleneck. CoRR Ankit Pensia, Varun S. Jog, Po-Ling Loh
2019	J	`jnl`	Introduction to the special issue for the ECML PKDD 2019 journal track. Data Min. Knowl. Discov. Karsten M. Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen
2019	J	`jnl`	Introduction to the special issue for the ECML PKDD 2019 journal track. Mach. Learn. Karsten M. Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen
2019	B	`conf`	Mean estimation for entangled single-sample distributions. ISIT Ankit Pensia, Varun S. Jog, Po-Ling Loh
2019	J	`jnl`	Robustifying deep networks for image segmentation. CoRR Zheng Liu, Jinnian Zhang, Varun S. Jog, Po-Ling Loh, Alan B. McMillan
2018	J	`jnl`	Adversarial Risk Bounds for Binary Classification via Function Transformation. CoRR Justin Khim, Po-Ling Loh
2018	J	`jnl`	Coding Theory for Inference, Learning and Optimization (Dagstuhl Seminar 18112). Dagstuhl Reports Po-Ling Loh, Arya Mazumdar, Dimitris S. Papailiopoulos, Rüdiger L. Urbanke
2018	B	`conf`	Generalization Error Bounds for Noisy, Iterative Algorithms. ISIT Ankit Pensia, Varun S. Jog, Po-Ling Loh
2018	J	`jnl`	Generalization Error Bounds for Noisy, Iterative Algorithms. CoRR Ankit Pensia, Varun S. Jog, Po-Ling Loh
2018	—	`conf`	Graph-Based Ascent Algorithms for Function Maximization. Allerton Muni Sreenivas Pydi, Varun S. Jog, Po-Ling Loh
2018	J	`jnl`	Graph-Based Ascent Algorithms for Function Maximization. CoRR Muni Sreenivas Pydi, Varun S. Jog, Po-Ling Loh
2018	B	`conf`	Online Learning with Graph-Structured Feedback against Adaptive Adversaries. ISIT Zhili Feng, Po-Ling Loh
2018	J	`jnl`	Online learning with graph-structured feedback against adaptive adversaries. CoRR Zhili Feng, Po-Ling Loh
2018	J	`jnl`	Persistence of centrality in random growing trees. Random Struct. Algorithms Varun S. Jog, Po-Ling Loh
2018	J	`jnl`	Scale calibration for high-dimensional robust regression. CoRR Po-Ling Loh
2017	J	`jnl`	Analysis of Centrality in Sublinear Preferential Attachment Trees via the Crump-Mode-Jagers Branching Process. IEEE Trans. Netw. Sci. Eng. Varun S. Jog, Po-Ling Loh
2017	J	`jnl`	Confidence Sets for the Source of a Diffusion in Regular Trees. IEEE Trans. Netw. Sci. Eng. Justin Khim, Po-Ling Loh
2017	B	`conf`	Information and estimation in Fokker-Planck channels. ISIT Andre Wibisono, Varun S. Jog, Po-Ling Loh
2017	J	`jnl`	Information and estimation in Fokker-Planck channels. CoRR Andre Wibisono, Varun S. Jog, Po-Ling Loh
2017	J	`jnl`	On Lower Bounds for Statistical Learning Theory. Entropy Po-Ling Loh
2017	J	`jnl`	Permutation Tests for Infection Graphs. CoRR Justin Khim, Po-Ling Loh
2016	J	`jnl`	Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process. CoRR Varun S. Jog, Po-Ling Loh
2016	—	`conf`	Collection, exploration and analysis of crowdfunding social networks. ExploreDB@SIGMOD/PODS Miao Cheng, Anand Sriramulu, Sudarshan Muralidhar, Boon Thau Loo, Laura Huang, Po-Ling Loh
2016	J	`jnl`	Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis. CoRR Justin Khim, Varun S. Jog, Po-Ling Loh
2016	—	`conf`	Computing and maximizing influence in linear threshold and triggering models. NIPS Justin T. Khim, Varun S. Jog, Po-Ling Loh
2015	J	`jnl`	Confidence Sets for the Source of a Diffusion in Regular Trees. CoRR Justin Khim, Po-Ling Loh
2015	J	`jnl`	Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence. CoRR Varun S. Jog, Po-Ling Loh
2015	B	`conf`	On model misspecification and KL separation for Gaussian graphical models. ISIT Varun S. Jog, Po-Ling Loh
2015	J	`jnl`	On model misspecification and KL separation for Gaussian graphical models. CoRR Varun S. Jog, Po-Ling Loh
2015	J	`jnl`	Persistence of centrality in random growing trees. CoRR Varun S. Jog, Po-Ling Loh
2015	—	`conf`	Recovering communities in weighted stochastic block models. Allerton Varun S. Jog, Po-Ling Loh
2015	J	`jnl`	Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima. J. Mach. Learn. Res. Po-Ling Loh, Martin J. Wainwright
2015	J	`jnl`	Statistical consistency and asymptotic normality for high-dimensional robust M-estimators. CoRR Po-Ling Loh
2014	—	`conf`	Concavity of reweighted Kikuchi approximation. NIPS Po-Ling Loh, Andre Wibisono
2014	J	`jnl`	High-dimensional learning of linear causal networks via inverse covariance estimation. J. Mach. Learn. Res. Po-Ling Loh, Peter Bühlmann
2014	—	`—`	High-dimensional statistics with systematically corrupted data. Po-Ling Loh
2014	J	`jnl`	Support recovery without incoherence: A case for nonconvex regularization. CoRR Po-Ling Loh, Martin J. Wainwright
2013	B	`conf`	Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates. ALT Po-Ling Loh, Sebastian Nowozin
2013	J	`jnl`	Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima CoRR Po-Ling Loh, Martin J. Wainwright
2013	—	`conf`	Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima. NIPS Po-Ling Loh, Martin J. Wainwright
2012	B	`conf`	Corrupted and missing predictors: Minimax bounds for high-dimensional linear regression. ISIT Po-Ling Loh, Martin J. Wainwright
2012	—	`conf`	Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. NIPS Po-Ling Loh, Martin J. Wainwright
2012	J	`jnl`	The Synthesis and Analysis of Stochastic Switching Circuits CoRR Hongchao Zhou, Po-Ling Loh, Jehoshua Bruck
2011	J	`jnl`	High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity CoRR Po-Ling Loh, Martin J. Wainwright
2011	—	`conf`	High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. NIPS Po-Ling Loh, Martin J. Wainwright
2009	B	`conf`	The robustness of stochastic switching networks. ISIT Po-Ling Loh, Hongchao Zhou, Jehoshua Bruck