Skip to content
conftrace_

matrix estimation

37 papers

ME

Papers

Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure ICLR 2025 Dimension-free Structured Covariance Estimation COLT 2024 Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation NeurIPS 2024 Learning the Positions in CountSketch ICLR 2023 MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation JMLR 2023 Matrix Estimation for Individual Fairness ICML 2023 Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning NeurIPS 2023 Differentially Private Covariance Revisited NeurIPS 2022 On Low-rank Trace Regression under General Sampling Distribution JMLR 2022 Recovering shared structure from multiple networks with unknown edge distributions JMLR 2022 A heuristic for statistical seriation UAI 2021 Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models NeurIPS 2021 Rank-one matrix estimation: analytic time evolution of gradient descent dynamics COLT 2021 Harnessing Structures for Value-Based Planning and Reinforcement Learning ICLR 2020 Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation NeurIPS 2020 Affine Invariant Covariance Estimation for Heavy-Tailed Distributions COLT 2019 ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation ICML 2019 On Robustness of Principal Component Regression NeurIPS 2019 A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models ICML 2018 Breaking the 1/√n Barrier: Faster Rates for Permutation-based Models in Polynomial Time COLT 2018 Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging JMLR 2018 Adaptive Clustering through Semidefinite Programming NeurIPS 2017 Matrix Norm Estimation from a Few Entries NeurIPS 2017 Minimax Estimation of Bandable Precision Matrices NeurIPS 2017 On Approximation Guarantees for Greedy Low Rank Optimization ICML 2017