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.
Improved locally private sparse estimation with multiple samples per user.
Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamenta…
Paper optimizes privacy-preserving distribution estimation for sparse data.
New framework tackles deep learning issues like local traps and miscalibration.
Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been conside…
Sparse-mode DMD disambiguates local and global modes in spatiotemporal data.
We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions. Our proposed algorithm integrates the proximal Newton algorithm with multi-stage convex relaxation based on the difference of convex (DC) programming, and enjoys both strong computational and statist…
Sparse hypergraph neural networks improve reasoning in large knowledge graphs.
Delta-AI speeds up inference in sparse PGMs by local credit assignment.
L0Learn solves sparse learning problems with millions of features.
The interaction between transitivity and sparsity, two common features in empirical networks, implies that there are local regions of large sparse networks that are dense. We call this the blessing of transitivity and it has consequences for both modeling and inference. Extant research suggests that statistical inferen…
This paper introduces an elasticity reconstruction method based on local displacement observations of elastic bodies. Sparse reconstruction theory is applied to formulate the underdetermined inverse problems of elasticity reconstruction including unobserved areas. An online local clustering scheme called a superelement…
A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing, there have only …
AGML model improves indoor localization with sparse fingerprints using meta-learning and graph neural networks.
With the rapid growth of data, distributed momentum stochastic gradient descent~(DMSGD) has been widely used in distributed learning, especially for training large-scale deep models. Due to the latency and limited bandwidth of the network, communication has become the bottleneck of distributed learning. Communication c…
New method discovers concepts in hidden feature layers using sparse subspace clustering.
New local-search methods close the gap in sparse tensor PCA.
We introduce in this paper the concept of tropical mirror hypersurfaces and we prove a complex tropical localization Theorem which is a version of Kapranov's Theorem \cite{K-00} in tropical geometry. We give a geometric and a topological equivalence between coamoebas of complex algebraic hypersurfaces defined by a maxi…
SDSPCAAN combines supervised and local data structures for better dimensionality reduction.
Paper develops sparse learning for heavy-tailed time series with locally stationary dynamics.
There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs. Gamarnik and Sudan (2014) showed that local algorithms are weaker than global algorithms for finding large independent sets in sparse random regular graphs. Montanari (2015) showed t…
Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurem…
NGSLL combines DNN accuracy with linear model interpretability.
SPARTAN learns sparse interaction graphs between objects in scenes.
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of…
Extends ESGVI for UWB localization with skewed noise, improving state estimation accuracy.
This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a priori unspecified background source, given only a single linear combination of the two sources. We propose a separation technique based on lo…
Bayesian method clusters data and selects variables with shrinkage priors.
New RL method uses distance between states instead of rewards for sparse reward environments.
Spectral clustering with edge counting detects communities in sparse models.
New techniques improve the accuracy of identifying nonlinear systems from noisy data.
Model financial default cascades on sparse graphs via hitting times.
Locally sparse neural networks improve interpretability for biomedical tabular data.
Proposes sparse local and regional counterfactual rules for robust recourses.
Locality regularized reconstruction finds sparse coefficients for sparse and structured data.
New PCA method handles multiple datasets and detects sparse patterns robustly.
We consider a distributed learning setup where a sparse signal is estimated over a network. Our main interest is to save communication resource for information exchange over the network and reduce processing time. Each node of the network uses a convex optimization based algorithm that provides a locally optimum soluti…
A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing, there have only …
We consider the decomposition of a data matrix assumed to be a superposition of a low-rank matrix and a component which is sparse in a known dictionary, using a convex demixing method. We consider two sparsity structures for the sparse factor of the dictionary sparse component, namely entry-wise and column-wise sparsit…
Concave regularization methods provide natural procedures for sparse recovery. However, they are difficult to analyze in the high dimensional setting. Only recently a few sparse recovery results have been established for some specific local solutions obtained via specialized numerical procedures. Still, the fundamental…
A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in…
RFA-LCF improves clustering accuracy by robustly handling noise and errors.
Network Lasso clusters sparse graph clusters efficiently.
As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the era of big data. Hen…
The paper introduces a method for interpretable principal component analysis of high-dimensional time series.
We propose a communication-efficient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size into machines, and estimates a local sparse LDA estimator on each machine using the data subset of size . After the distri…
Paper tackles robust Euclidean distance estimation with sparse outliers.
New method upsamples sparse, non-uniform point clouds more accurately.