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Found 17 papers, 1 papers with code
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization
This linear convergence result in the over-parameterization case is especially significant because one can apply the asymmetric parameterization to the symmetric setting to speed up from $\Omega (1/T^2)$ to linear convergence.
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning.
A Validation Approach to Over-parameterized Matrix and Image Recovery
In this paper, we study the problem of recovering a low-rank matrix from a number of noisy random linear measurements.
Flat minima generalize for low-rank matrix recovery
Empirical evidence suggests that for a variety of overparameterized nonlinear models, most notably in neural network training, the growth of the loss around a minimizer strongly impacts its performance.
Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization
We study the asymmetric matrix factorization problem under a natural nonconvex formulation with arbitrary overparametrization.
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
We study the robust recovery of a low-rank matrix from sparsely and grossly corrupted Gaussian measurements, with no prior knowledge on the intrinsic rank.
TenIPS: Inverse Propensity Sampling for Tensor Completion
Tensors are widely used to represent multiway arrays of data.
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
The theorems show that a relatively sharper regularizer leads to a tighter error bound, which is consistent with our numerical results.
Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle
Existing results for low-rank matrix recovery largely focus on quadratic loss, which enjoys favorable properties such as restricted strong convexity/smoothness (RSC/RSM) and well conditioning over all low rank matrices.
$k$FW: A Frank-Wolfe style algorithm with stronger subproblem oracles
This paper proposes a new variant of Frank-Wolfe (FW), called $k$FW.
On the simplicity and conditioning of low rank semidefinite programs
It is more challenging to show that an approximate solution to the SDP formulated with noisy problem data acceptably solves the original problem; arguments are usually ad hoc for each problem setting, and can be complex.
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
Compared to the max norm and the factored formulation of the nuclear norm, factor group-sparse regularizers are more efficient, accurate, and robust to the initial guess of rank.
Bundle Method Sketching for Low Rank Semidefinite Programming
In this paper, we show that the bundle method can be applied to solve semidefinite programming problems with a low rank solution without ever constructing a full matrix.
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
The task of recovering a low-rank matrix from its noisy linear measurements plays a central role in computational science.
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity
This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form.
Frank-Wolfe Style Algorithms for Large Scale Optimization
We introduce a few variants on Frank-Wolfe style algorithms suitable for large scale optimization.
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis
In this paper, we introduce a powerful technique based on Leave-one-out analysis to the study of low-rank matrix completion problems.
