{"total":30,"rows":[{"id":"2203.02839","title":"Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization","created":"2022-03-06","cited_by_count":34,"area":null},{"id":"2302.01186","title":"The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing","created":"2023-02-02","cited_by_count":33,"area":null},{"id":"2601.13474","title":"Preconditioning Benefits of Spectral Orthogonalization in Muon","created":"2026-01-20","cited_by_count":30,"area":null},{"id":"2109.11154","title":"Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery","created":"2021-09-23","cited_by_count":22,"area":null},{"id":"2202.08788","title":"Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization","created":"2022-02-17","cited_by_count":21,"area":null},{"id":"2206.03345","title":"Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification","created":"2022-06-07","cited_by_count":21,"area":null},{"id":"2310.01769","title":"How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization","created":"2023-10-03","cited_by_count":18,"area":null},{"id":"2206.08756","title":"Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay","created":"2022-06-17","cited_by_count":15,"area":null},{"id":"2207.01789","title":"Improved Global Guarantees for the Nonconvex Burer--Monteiro Factorization via Rank Overparameterization","created":"2022-07-05","cited_by_count":13,"area":null},{"id":"2102.02969","title":"Sign-RIP: A Robust Restricted Isometry Property for Low-rank Matrix Recovery","created":"2021-02-05","cited_by_count":10,"area":null},{"id":"2209.10675","title":"A Validation Approach to Over-parameterized Matrix and Image Recovery","created":"2022-09-21","cited_by_count":8,"area":null},{"id":"2401.11940","title":"Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent","created":"2024-01-22","cited_by_count":7,"area":null},{"id":"2208.11246","title":"Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion","created":"2022-08-24","cited_by_count":6,"area":null},{"id":"2407.00490","title":"Toward Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixture Models","created":"2024-06-29","cited_by_count":6,"area":null},{"id":"2104.10790","title":"Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime","created":"2021-04-21","cited_by_count":5,"area":null},{"id":"2106.06153","title":"Towards Understanding Generalization via Decomposing Excess Risk Dynamics","created":"2021-06-11","cited_by_count":5,"area":null},{"id":"2305.17224","title":"Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent","created":"2023-05-26","cited_by_count":5,"area":null},{"id":"2210.00346","title":"Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition","created":"2022-10-01","cited_by_count":4,"area":null},{"id":"2305.06199","title":"Computationally Efficient and Statistically Optimal Robust High-Dimensional Linear Regression","created":"2023-05-10","cited_by_count":4,"area":null},{"id":"2502.00463","title":"Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent","created":"2025-02-01","cited_by_count":4,"area":null},{"id":"2408.13276","title":"Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity","created":"2024-08-20","cited_by_count":3,"area":null},{"id":"2510.01175","title":"On the Benefits of Weight Normalization for Overparameterized Matrix Sensing","created":"2025-10-01","cited_by_count":3,"area":null},{"id":"2403.01420","title":"The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing","created":"2024-03-03","cited_by_count":2,"area":null},{"id":"2303.11453","title":"Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing","created":"2023-03-20","cited_by_count":1,"area":null},{"id":"2310.15549","title":"Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing","created":"2023-10-24","cited_by_count":1,"area":null},{"id":"2512.07490","title":"Efficient Low-Tubal-Rank Tensor Estimation via Alternating Preconditioned Gradient Descent","created":"2025-12-08","cited_by_count":1,"area":null},{"id":"2202.04219","title":"Improving Computational Complexity in Statistical Models with Second-Order Information","created":"2022-02-09","cited_by_count":0,"area":null},{"id":"2604.13393","title":"A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity","created":"2026-04-15","cited_by_count":0,"area":null},{"id":"2606.31390","title":"Direction-Magnitude Decomposition for Low-Rank Matrix Optimization: Faster Convergence and Saddle-to-saddle Dynamics","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2609.37865","title":"Strict-Saddle Landscapes and Multi-Rank Geometry in Low-Tubal-Rank Tensor Sensing","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}