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.
The paper proposes methods for predicting missing values in mixed data matrices.
New method for robust matrix completion with mixed data types.
Method completes mixed matrix from complex surveys with heterogeneous missingness.
Inductive Matrix Completion (IMC) is an important class of matrix completion problems that allows direct inclusion of available features to enhance estimation capabilities. These models have found applications in personalized recommendation systems, multilabel learning, dictionary learning, etc. This paper examines a g…
This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This under-complete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with an unknown orthogonal mixing matrix. This issue is formulated in a…
Unified framework for hyperbolic embeddings from mixed data types.
New Bayesian matrix completion method using Stiefel manifolds.
Novel LRMC tackles missing data and outliers in large-scale low-rank data recovery.
New Riemannian optimization improves variance estimation in mixed models.
New method for mixed memberships using symmetrized Laplacian inverse matrix.
Novel network model estimates mixed-membership structure with covariate information.
Study on Gaussian ensemble of matrix products with mixed moments computed.
Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. Here, we derive a global optimization (GOP) algor…
New algorithm predicts missing matrix entries using side information, outperforming existing methods.
Study on mixed Killing vector fields on Cigar Ricci-Bourguignon solitons.
L21 SNF compresses mixed-sign data robustly.
A new method for community detection in networks is presented.
Identifying components and estimating mixing weights in unlabeled finite mixtures under marginal independence.
Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into product of two non-negative matrices, namely a signal matrix and a mixing matrix. NMF suffers from the scale and ordering ambiguities. Often, the source signals can be monotonous in nature. For example, in source separation problem, the source…
DPERC efficiently estimates covariance matrices for mixed data with missing values.
Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior knowledge of the mixing filters is available. Both individual sources and mixing filters need to be estimated. In addition, a mixture may con…
A new model estimates mixed memberships for categorical data with weighted responses.
Mixed-precision CA-SGD for generalized linear models on GPUs
DiMMSB models directed mixed membership networks, identifying distinct community structures.
Study on robustness of unsupervised representation learning in slightly misspecified settings.
Unified formula for training dynamics of linear networks combining lazy and balanced regimes.
We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix map…
MMM model clusters mixed-type longitudinal data efficiently.
Unified framework for nonconvex matrix completion with linearly parameterized factors.
We address the problem of estimating the mixing time of a Markov chain from a single trajectory of observations. Unlike most previous works which employed Hilbert space methods to estimate spectral gaps, we opt for an approach based on contraction with respect to total variation. Specifically, we estimate the contracti…
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…
New findings show pure strategy equilibria are more robust in a war of attrition game.
Proximal operators are of particular interest in optimization problems dealing with non-smooth objectives because in many practical cases they lead to optimization algorithms whose updates can be computed in closed form or very efficiently. A well-known example is the proximal operator of the vector norm, whic…
A very simple interpretation of matrix completion problem is introduced based on statistical models. Combined with the well-known results from missing data analysis, such interpretation indicates that matrix completion is still a valid and principled estimation procedure even without the missing completely at random (M…
Recovering low-rank and sparse matrices from incomplete or corrupted observations is an important problem in machine learning, statistics, bioinformatics, computer vision, as well as signal and image processing. In theory, this problem can be solved by the natural convex joint/mixed relaxations (i.e., l_{1}-norm and tr…
Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of rati…
Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can …
New method corrects bias in missing data for matrix completion.
Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study ma…
Study improves fractional posterior for 1-bit matrix completion.
Paper develops new patterns for unique matrix completions.
A new method for 1-bit matrix completion that is faster and more accurate.
We consider the problem of matrix completion with side information (\textit{inductive matrix completion}). In real-world applications many side-channel features are typically non-informative making feature selection an important part of the problem. We incorporate feature selection into inductive matrix completion by p…
Study shows how fast a specific matrix completion method works.
Proposes a transductive matrix completion method with calibration for multi-task learning.
Paper explores robustness of CCS model for matrix completion.
In this paper, we develop a relative error bound for nuclear norm regularized matrix completion, with the focus on the completion of full-rank matrices. Under the assumption that the top eigenspaces of the target matrix are incoherent, we derive a relative upper bound for recovering the best low-rank approximation of t…
New method solves matrix completion problems to certifiable optimality.