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.
Improves SVGP methods for faster and more accurate Gaussian process inference.
Network Lasso clusters sparse graph clusters efficiently.
A method to improve sequential learning by keeping past data errors in check.
In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e.g., random projection, random hashing), for large-scale high-dimensional classification. Previous theoretical results on randomized reduction methods hinge on strong assumptio…
DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.
Optimizes subset selection in sparse learning problems.
Iterative Hard Thresholding (IHT) is a class of projected gradient descent methods for optimizing sparsity-constrained minimization models, with the best known efficiency and scalability in practice. As far as we know, the existing IHT-style methods are designed for sparse minimization in primal form. It remains open t…
We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because of the dual formulation. More…
In this paper, the dual Orlicz curvature measure is proposed and its basic properties are provided. A variational formula for the dual Orlicz-quermassintegral is established in order to give a geometric interpretation of the dual Orlicz curvature measure. Based on the established variational formula, a solution to the …
Solves dual imbalance in detecting sparse anomalies in MIL.
Canonical correlation analysis (CCA) is a multivariate statistical technique for finding the linear relationship between two sets of variables. The kernel generalization of CCA named kernel CCA has been proposed to find nonlinear relations between datasets. Despite their wide usage, they have one common limitation that…
Dual-sPLS improves feature selection and prediction in high-dimensional data.
DualVDT improves time-series forecasting with a novel dual reparametrized structure.
Efficient algorithm solves best subset selection problem.
Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries. Safe screening, which eliminates the features that are guaranteed to have zero coefficients for a certain value of the regularization parameter, is a technique for improving t…
Random extrapolation speeds up coordinate descent for sparse and dense data.
Method converts neural networks to function space for better uncertainty quantification.
New algorithm speeds up large-scale statistical inference.
This paper develops a general theoretical framework to analyze structured sparse recovery problems using the notation of dual certificate. Although certain aspects of the dual certificate idea have already been used in some previous work, due to the lack of a general and coherent theory, the analysis has so far only be…
Solves a generalized dual Minkowski problem for specific values of q.
A new method solves large-scale sparse group square-root Lasso problems efficiently.
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations. We develop a novel algorithm of primal-dual active set type for a class of nonconvex sparsity-promoting penalties, including , bridge, smoothly clipped absolute deviation, capped and mini…
A new method reduces feature size in CRFs for faster training.
Paper develops efficient variational inference for sparse deep learning with theoretical guarantees.
Dual averaging-type methods are widely used in industrial machine learning applications due to their ability to promoting solution structure (e.g., sparsity) efficiently. In this paper, we propose a novel accelerated dual-averaging primal-dual algorithm for minimizing a composite convex function. We also derive a stoch…
We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view…
New method learns sparse distributions by thresholding samples, improving performance and efficiency.
Optimal hedging framework with variational preferences under convex risk measures.
The paper improves Gaussian process models for efficient batch optimization.
Adaptive dropout and regularization are shown to be dual in linear networks.
Study iterative regularization for linear models with convex bias, improving robust sparse recovery.
Entropic regularization is quickly emerging as a new standard in optimal transport (OT). It enables to cast the OT computation as a differentiable and unconstrained convex optimization problem, which can be efficiently solved using the Sinkhorn algorithm. However, entropy keeps the transportation plan strictly positive…
One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization. However, most existing works in this direction learn a sparse combination of tensors. In this work, we fill this gap by proposing a variant of the latent trace norm that helps in learning a non-sparse combinatio…
The paper analyzes uncertainty quantification in sparse Gaussian process regression with a Brownian motion prior.
Regular variation provides a convenient theoretical framework to study large events. In the multivariate setting, the dependence structure of the positive extremes is characterized by a measure - the spectral measure - defined on the positive orthant of the unit sphere. This measure gathers information on the localizat…
Develops SGP-VAE for efficient sparse GP inference in multi-dimensional datasets.
New method for hyperparameter tuning in sparse matrix factorization.
This work proposes a novel method for semi-supervised learning from partially labeled massive network-structured datasets, i.e., big data over networks. We model the underlying hypothesis, which relates data points to labels, as a graph signal, defined over some graph (network) structure intrinsic to the dataset. Follo…
While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP approximations that exploit the sparse spectral representation of a GP kernel. This paper presents such a…
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…
PURE-CD algorithm proves complexity bounds for convex-concave problems.
SVGP KAN integrates sparse variational GP with KANs for scalable probabilistic inference.
Paper proposes a method to improve variational inference for sparse networks.
Modeling sequential data has become more and more important in practice. Some applications are autonomous driving, virtual sensors and weather forecasting. To model such systems, so called recurrent models are frequently used. In this paper we introduce several new Deep recurrent Gaussian process (DRGP) models based on…
Paper tightens variational GP approximations for large datasets.
Improved sparse Gaussian processes using structured scaling matrices and Power-EP framework.
A new method for efficient Gaussian process inference using sparse approximations.
Recently, a number of mostly -norm regularized least squares type deterministic algorithms have been proposed to address the problem of \emph{sparse} adaptive signal estimation and system identification. From a Bayesian perspective, this task is equivalent to maximum a posteriori probability estimation under a …