A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New spectral methods improve matrix estimation in RL with low-rank structure.
Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model w…
New nonconvex regularizer speeds up low-rank matrix completion.
New method improves robust low-rank matrix completion for computer vision.
New approach to convex hulls for low-rank problems.
Proposes a new model for image restoration combining deep learning and total variation.
CoreFlow models matrix-valued distributions efficiently, preserving shared low-rank structure.
FLAMBE tackles RL in low rank MDPs by learning features.
UA-LQE improves value function learning by selectively erasing uncertain entries in Q-matrix.
Study the distribution for low-rank matrix learning, improving inference methods.
New framework explains why nonconvex methods work well in low-rank matrix estimation.
Paper develops a new weighted low-rank matrix approximation technique.
In this paper, we consider the problem of low-rank phase retrieval whose objective is to estimate a complex low-rank matrix from magnitude-only measurements. We propose a hierarchical prior model for low-rank phase retrieval, in which a Gaussian-Wishart hierarchical prior is placed on the underlying low-rank matrix to …
In the probabilistic topic models, the quantity of interest---a low-rank matrix consisting of topic vectors---is hidden in the text corpus matrix, masked by noise, and the Singular Value Decomposition (SVD) is a potentially useful tool for learning such a low-rank matrix. However, the connection between this low-rank m…
New method normalizes matrix features for robust low-rank approximation.
ScaledGD improves gradient descent for ill-conditioned low-rank matrix estimation.
We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices. A salient feature of the proposed factorization scheme is it decouples the low-rank and the structural constraints onto separate factors. We formulate the optim…
New method solves nonsmooth low-rank matrix optimization problems efficiently.
We develop latent variable models for Bayesian learning based low-rank matrix completion and reconstruction from linear measurements. For under-determined systems, the developed methods are shown to reconstruct low-rank matrices when neither the rank nor the noise power is known a-priori. We derive relations between th…
Gradient descent recovers low-rank matrices from corrupted measurements with double over-parameterization.
Novel method for efficient low-rank matrix estimation and bandit algorithms.
As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs. The learned low-dimensional node vector representation is generalizable and eases the knowledge discove…
Low-rank matrix recovery has found many applications in science and engineering such as machine learning, signal processing, collaborative filtering, system identification, and Euclidean embedding. But the low-rank matrix recovery problem is an NP hard problem and thus challenging. A commonly used heuristic approach is…
The problem of low rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a W…
GD learns matrix solutions incrementally, revealing insights into generalization.
SGD with mini-batches can solve convex low-rank matrix problems efficiently.
Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix completion assume that the input partially-observed matrix is low rank. The success of these methods…
Paper studies asymmetric matrix sensing, proving gradient descent converges to low-rank solutions.
Paper develops new patterns for unique matrix completions.
LEARNER improves low-rank matrix estimation using source population data.
Equivalent formulations for low-rank matrix optimization are proven.
Improved convergence for overparameterized low-rank matrix sensing.
New approach uses compressible dynamics to train deep models efficiently.
Develops PRPCA for smooth image recovery combining low-rank and smoothness.
The paper examines how gradient descent stabilizes low-rank matrix factorization in noisy conditions.
Unified approach for robust low rank matrix estimation with adversaries.
Consider a movie recommendation system where apart from the ratings information, side information such as user's age or movie's genre is also available. Unlike standard matrix completion, in this setting one should be able to predict inductively on new users/movies. In this paper, we study the problem of inductive matr…
We propose a unified framework for estimating low-rank matrices through nonconvex optimization based on gradient descent algorithm. Our framework is quite general and can be applied to both noisy and noiseless observations. In the general case with noisy observations, we show that our algorithm is guaranteed to linearl…
We present a unified framework for low-rank matrix estimation with nonconvex penalties. We first prove that the proposed estimator attains a faster statistical rate than the traditional low-rank matrix estimator with nuclear norm penalty. Moreover, we rigorously show that under a certain condition on the magnitude of t…
Most recent results in matrix completion assume that the matrix under consideration is low-rank or that the columns are in a union of low-rank subspaces. In real-world settings, however, the linear structure underlying these models is distorted by a (typically unknown) nonlinear transformation. This paper addresses the…
Paper proposes fast, robust methods for low-rank matrix recovery.
Matrices of (approximate) low rank are pervasive in data science, appearing in recommender systems, movie preferences, topic models, medical records, and genomics. While there is a vast literature on how to exploit low rank structure in these datasets, there is less attention on explaining why the low rank structure ap…
The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion. The success of ALOHA is due to the concise signal representation in the k-space domain thanks to…
Gradient descent solves asymmetric low-rank matrix sensing without balancing.
Novel LRMC tackles missing data and outliers in large-scale low-rank data recovery.
Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization pro…
Kernel methods are widespread in machine learning; however, they are limited by the quadratic complexity of the construction, application, and storage of kernel matrices. Low-rank matrix approximation algorithms are widely used to address this problem and reduce the arithmetic and storage cost. However, we observed tha…
New algorithm improves deep learning models' robustness without sacrificing accuracy.