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 method for hyperparameter tuning in sparse matrix factorization.
In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…
Study confirms sparse coding in whole brain using MRI data.
High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this paper, we are interested in a broad class of sparse learning approaches formulated as linear programs parametrized by a {\em regularization factor}, and solve them by the parametric simplex method (PSM). O…
Sparse APCA identifies sparse factors in financial returns over time.
The CUR decomposition provides an approximation of a matrix that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns of . In this regard, it appears to be similar to many sparse PCA methods. However, CUR takes a randomized algorithm…
Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes. Different from most…
A new hashing method improves accuracy by learning an orthogonal transform.
Proposes a method to learn sparse deep neural networks with theoretical guarantees.
A new method for sparse PCA using orthogonal rotations and soft-thresholding.
Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate the sparse signal of interest and the sparse noise of no interest.…
New combinatorial method for sparse PCA works beyond spiked identity model.
This paper considers the sparse eigenvalue problem, which is to extract dominant (largest) sparse eigenvectors with at most non-zero components. We propose a simple yet effective solution called truncated power method that can approximately solve the underlying nonconvex optimization problem. A strong sparse recove…
New method improves robust sparse association estimation.
We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By co…
Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to process nonnegative tensor. Sometimes, additional sparse regularization is needed to extract meaningful nonnegative and sparse components. Thus, an optimization method for NCP that can impose sparsity efficiently is required. In this paper, we co…
Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the m…
New method combines spectral and sparse methods for Gaussian processes.
Sparse coding has shown its power as an effective data representation method. However, up to now, all the sparse coding approaches are limited within the single domain learning problem. In this paper, we extend the sparse coding to cross domain learning problem, which tries to learn from a source domain to a target dom…
Study identifies methods for handling sparse data in web settings with contextual bandits.
This paper converts ADMM to proximal gradient for efficient sparse estimation.
We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with this type of data is ``sparse multiple canonical correlation analysis'' (sparse mCCA). All of the current sparse mCCA t…
New method learns sparse distributions by thresholding samples, improving performance and efficiency.
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize bo…
IRKSN algorithm achieves sparse recovery with wider applicability conditions.
Paper proposes a learning-based sparse Bayesian method for accurate off-grid DOA estimation.
ADAPT method controls balance between sparse generator and discriminator in GANs.
We propose a method for solving statistical mechanics problems defined on sparse graphs. It extracts a small Feedback Vertex Set (FVS) from the sparse graph, converting the sparse system to a much smaller system with many-body and dense interactions with an effective energy on every configuration of the FVS, then learn…
New method finds sparse networks without labels, improving performance.
New methods show sparse portfolios offer no advantage over mean-variance in diversification.
New sparse penalty improves biclustering for gene expression data.
We present a comprehensive framework for structured sparse coding and modeling extending the recent ideas of using learnable fast regressors to approximate exact sparse codes. For this purpose, we develop a novel block-coordinate proximal splitting method for the iterative solution of hierarchical sparse coding problem…
Principal Component Analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance…
Unified framework for constructing nonconvex sparse recovery methods.
A new method scales sparse machine learning to ultra-high dimensional problems.
This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size of the sa…
New method discovers causal relationships in sparse linear data.
Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples a…
Efficiently selects predictors in sparse regression without approximations.
Paper develops IFTRR to solve sparse generalized eigenvalue problems efficiently.
Quantum algorithm improves sparse vector recovery from noisy measurements.
Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing …
The presence of a sparse "truth" has been a constant assumption in the theoretical analysis of sparse PCA and is often implicit in its methodological development. This naturally raises questions about the properties of sparse PCA methods and how they depend on the assumption of sparsity. Under what conditions can the r…
HARFE approximates sparse additive functions using random features and ridge regression.
The paper analyzes methods for sparse Bayesian regression in nonlinear system identification.
Method generates dense fields from sparse measurements without needing spatial statistics or examples.
Two new methods improve block-sparse signal recovery from noisy data.
Proposes efficient Bayesian logistic regression for large sparse datasets.