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 GPU kernels boost deep learning speed and memory efficiency.
SKI accelerates GP inference with sparse grids to handle higher dimensions.
New method tackles high-dimensional SBL without covariance matrices.
Multivariate regression model is a natural generalization of the classical univari- ate regression model for fitting multiple responses. In this paper, we propose a high- dimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accoun…
Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models. Inducing sparseness as a way to reduce the memory and computation footprint of these models has seen significant research attention in recent years. In this paper, w…
Solution to sparse PCA tuning problem using Empirical Bayes.
New method trains sparse Gaussian processes without matrix inversion.
This paper proposes a new algorithm for multiple sparse regression in high dimensions, where the task is to estimate the support and values of several (typically related) sparse vectors from a few noisy linear measurements. Our algorithm is a "forward-backward" greedy procedure that -- uniquely -- operates on two disti…
New neural KB representation speeds up reasoning with large symbolic knowledge bases.
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 reduces matrix multiplication time for sparse matrices.
We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. These components can be computed one by one, repeatedly solving the single-component problem and def…
We compute an approximate Fréchet mean for sets of sparse graphs.
New method improves robust low-rank matrix completion for computer vision.
Sparse OSEs achieve optimal embedding dimension of O(d).
Paper introduces a novel matrix-wise sparse MNNLS formulation and algorithm.
Sparse GCA finds linear relationships in multiple datasets, using gradient descent.
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs). Computations using sparse matrices obtained by pruning parameters, however, exhibit vastly different parallelism depending on the index representation scheme. As a result, fine-grained pruning ha…
Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and known. The formulation counts sparse PCA with multiple factors, subspace clustering and low-rank sparse bilinear regression as potential ap…
Origin-destination (OD) matrices are often used in urban planning, where a city is partitioned into regions and an element (i, j) in an OD matrix records the cost (e.g., travel time, fuel consumption, or travel speed) from region i to region j. In this paper, we partition a day into multiple intervals, e.g., 96 15-min …
This letter proposes a dictionary learning algorithm for blind one bit compressed sensing. In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. In this context, the multiplication of measurement matrix $\Ab$ an…
A new method for sparse PCA using orthogonal rotations and soft-thresholding.
The paper infers multiple graphs from stationary signals on them.
New method speeds up sparse Bayesian learning without covariance matrix.
NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.
Efficient private matrix analysis algorithms for recent variants.
New method infers co-expression networks robustly from multiple studies.
The modified Cholesky decomposition is commonly used for precision matrix estimation given a specified order of random variables. However, the order of variables is often not available or cannot be pre-determined. In this work, we propose to address the variable order issue in the modified Cholesky decomposition for sp…
New method for hyperparameter tuning in sparse matrix factorization.
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered from linear measurements. We present a unified framework for S-NNLS based on a rectified power exponential scale mixture prior on the spars…
In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversio…
Paper tackles sparse recovery with shuffled labels, establishing statistical and computational limits.
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. I…
Matrix completion constantly receives tremendous attention from many research fields. It is commonly applied for recommender systems such as movie ratings, computer vision such as image reconstruction or completion, multi-task learning such as collaboratively modeling time-series trends of multiple sensors, and many ot…
NeuralIF uses neural networks to improve preconditioning for faster CG convergence.
This paper examines fundamental error characteristics for a general class of matrix completion problems, where the matrix of interest is a product of two a priori unknown matrices, one of which is sparse, and the observations are noisy. Our main contributions come in the form of minimax lower bounds for the expected pe…
Bayesian model improves categorization of explosions from sparse data.
New algorithms reduce communication in GNN training.
New method solves sparse PCA for multiple components efficiently.
New algorithm finds sparse matrices on Stiefel manifold for optimisation.
Paper develops a decoder for sparse codes without encoder matrix, achieving optimal recovery.
A framework estimates multiple precision matrices with shared structures.
Methodology for learning sparse models using all multiplicative interactions efficiently.
The paper improves DFA for CNN and RNN training to match BP accuracy.
This work presents a general framework for solving the low rank and/or sparse matrix minimization problems, which may involve multiple non-smooth terms. The Iteratively Reweighted Least Squares (IRLS) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables an…
We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for data integration, based on combining multiple matrix factorisation methods, that can be used for in- and out-of-matrix prediction of missing values. The model is very general and can be used to integrate many datasets across different entity type…
Ranky solves SVD for large sparse matrices in distributed systems.
A tuning-free method recovers jointly sparse signals in MMV using implicit regularization.