Timezone: »
Poster
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen · Pan Xu · Lingxiao Wang · Jian Ma · Quanquan Gu
We propose a nonconvex estimator for the covariate adjusted precision matrix estimation problem in the high dimensional regime, under sparsity constraints. To solve this estimator, we propose an alternating gradient descent algorithm with hard thresholding. Compared with existing methods along this line of research, which lack theoretical guarantees in optimization error and/or statistical error, the proposed algorithm not only is computationally much more efficient with a linear rate of convergence, but also attains the optimal statistical rate up to a logarithmic factor. Thorough experiments on both synthetic and real data support our theory.
Author Information
Jinghui Chen (University of Virginia)
Pan Xu (University of California, Los Angeles)
Lingxiao Wang (UCLA)
Jian Ma (Carnegie Mellon University)
Quanquan Gu (UCLA)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Oral: Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization »
Wed. Jul 11th 12:50 -- 01:10 PM Room K11
More from the Same Authors
-
2022 Poster: Langevin Monte Carlo for Contextual Bandits »
Pan Xu · Hongkai Zheng · Eric Mazumdar · Kamyar Azizzadenesheli · Animashree Anandkumar -
2022 Spotlight: Langevin Monte Carlo for Contextual Bandits »
Pan Xu · Hongkai Zheng · Eric Mazumdar · Kamyar Azizzadenesheli · Animashree Anandkumar -
2021 Poster: Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits »
Tianyuan Jin · Jing Tang · Pan Xu · Keke Huang · Xiaokui Xiao · Quanquan Gu -
2021 Poster: MOTS: Minimax Optimal Thompson Sampling »
Tianyuan Jin · Pan Xu · Jieming Shi · Xiaokui Xiao · Quanquan Gu -
2021 Spotlight: Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits »
Tianyuan Jin · Jing Tang · Pan Xu · Keke Huang · Xiaokui Xiao · Quanquan Gu -
2021 Spotlight: MOTS: Minimax Optimal Thompson Sampling »
Tianyuan Jin · Pan Xu · Jieming Shi · Xiaokui Xiao · Quanquan Gu -
2020 Poster: A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation »
Pan Xu · Quanquan Gu -
2018 Poster: Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow »
Xiao Zhang · Simon Du · Quanquan Gu -
2018 Poster: Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions »
Pan Xu · Tianhao Wang · Quanquan Gu -
2018 Oral: Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow »
Xiao Zhang · Simon Du · Quanquan Gu -
2018 Oral: Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions »
Pan Xu · Tianhao Wang · Quanquan Gu -
2018 Poster: A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery »
Xiao Zhang · Lingxiao Wang · Yaodong Yu · Quanquan Gu -
2018 Poster: Stochastic Variance-Reduced Hamilton Monte Carlo Methods »
Difan Zou · Pan Xu · Quanquan Gu -
2018 Oral: Stochastic Variance-Reduced Hamilton Monte Carlo Methods »
Difan Zou · Pan Xu · Quanquan Gu -
2018 Oral: A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery »
Xiao Zhang · Lingxiao Wang · Yaodong Yu · Quanquan Gu -
2018 Poster: Stochastic Variance-Reduced Cubic Regularized Newton Method »
Dongruo Zhou · Pan Xu · Quanquan Gu -
2018 Oral: Stochastic Variance-Reduced Cubic Regularized Newton Method »
Dongruo Zhou · Pan Xu · Quanquan Gu -
2017 Poster: Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference »
Aditya Chaudhry · Pan Xu · Quanquan Gu -
2017 Poster: High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm »
Rongda Zhu · Lingxiao Wang · Chengxiang Zhai · Quanquan Gu -
2017 Poster: Robust Gaussian Graphical Model Estimation with Arbitrary Corruption »
Lingxiao Wang · Quanquan Gu -
2017 Talk: High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm »
Rongda Zhu · Lingxiao Wang · Chengxiang Zhai · Quanquan Gu -
2017 Talk: Robust Gaussian Graphical Model Estimation with Arbitrary Corruption »
Lingxiao Wang · Quanquan Gu -
2017 Talk: Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference »
Aditya Chaudhry · Pan Xu · Quanquan Gu -
2017 Poster: A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery »
Lingxiao Wang · Xiao Zhang · Quanquan Gu -
2017 Talk: A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery »
Lingxiao Wang · Xiao Zhang · Quanquan Gu