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.
Unified framework for pattern recovery in penalized and thresholded estimation.
Paper proposes efficient algorithm for recovering sparsity pattern from deterministic missing data.
Empirical study on UEEs reveals liquidity's role and universal recovery patterns.
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…
Our work is focused on the joint sparsity recovery problem where the common sparsity pattern is corrupted by Poisson noise. We formulate the confidence-constrained optimization problem in both least squares (LS) and maximum likelihood (ML) frameworks and study the conditions for perfect reconstruction of the original r…
Binary feedback outperforms ordinal comparisons in ranking recovery.
New method optimizes MRI sampling patterns for faster scans.
We study the property of the Fused Lasso Signal Approximator (FLSA) for estimating a blocky signal sequence with additive noise. We transform the FLSA to an ordinary Lasso problem. By studying the property of the design matrix in the transformed Lasso problem, we find that the irrepresentable condition might not hold, …
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and sol…
This paper considers the recovery of a low-rank matrix from an observed version that simultaneously contains both (a) erasures: most entries are not observed, and (b) errors: values at a constant fraction of (unknown) locations are arbitrarily corrupted. We provide a new unified performance guarantee on when the natura…
Graph conformal prediction predicts future power outages with high confidence.
New method selects variables in groups with few nonzeros, improving support recovery.
This work improves dictionary learning speed without sacrificing accuracy.
We consider the problem of recovering block-sparse signals whose structures are unknown \emph{a priori}. Block-sparse signals with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. However, the knowledge of the block structure is usually unavailable in practice. In this paper, we d…
A new model explains U- and Swoosh-shaped stock price recovery during the COVID-19.
Researchers improve tree model recovery from noisy data.
New method improves traffic data recovery for streaming data.
The study uses Random Matrix Theory to identify structural changes in stock markets during shocks.
New method for tensor completion from specific mode observations.
SMM improves signal recovery from noisy data.
Standard compressive sensing results state that to exactly recover an s sparse signal in R^p, one requires O(s. log(p)) measurements. While this bound is extremely useful in practice, often real world signals are not only sparse, but also exhibit structure in the sparsity pattern. We focus on group-structured patterns …
Paper proposes a new method for exact recovery in robust tensor principal component analysis.
Estimates treatment effects in panel data with general intervention patterns.
A new associative memory uses Sinkhorn divergence for efficient pattern retrieval.
Unified theory explains housing cycle across metros, showing credit expansion impacts.
New framework extends ICA for non-independent variables, identifying pairwise mean independence.
Auto-Encoders are unsupervised models that aim to learn patterns from observed data by minimizing a reconstruction cost. The useful representations learned are often found to be sparse and distributed. On the other hand, compressed sensing and sparse coding assume a data generating process, where the observed data is g…
Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from the observed sum. Such additive decompositions have applications in a variety of numerical problems including system identification, latent var…
Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture mu…
This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI). Each temporal-domain MR image is viewed as a point that lies onto or close to a smooth manifold, and landmark …
Sparse mapping has been a key methodology in many high-dimensional scientific problems. When multiple tasks share the set of relevant features, learning them jointly in a group drastically improves the quality of relevant feature selection. However, in practice this technique is used limitedly since such grouping infor…
In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently…
This paper is concerned with jointly recovering node-variables from a collection of pairwise difference measurements. Imagine we acquire a few observations taking the form of ; the observation pattern is represented by a measurement graph with an ed…
We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a condition on existence of novel-words that are unique to each topic. We present a suite of highly efficient algorithms based…
This study develops a dynamic inverse optimization framework to recover hidden, time-varying preferences from observed allocation trajectories.
Framework for inferring latent structure from sparse, imperfectly detected bipartite networks.
This work tackles community detection in networks with node attributes, achieving exact recovery.
A new framework detects changepoints in complex data.
New method solves sparse approximation problem using trimmed lasso and generalized soft-min penalties.
New matrix completion method for arbitrary sampling patterns using network flows.
We are motivated by problems that arise in a number of applications such as Online Marketing and Explosives detection, where the observations are usually modeled using Poisson statistics. We model each observation as a Poisson random variable whose mean is a sparse linear superposition of known patterns. Unlike many co…
Current studies about motor imagery based rehabilitation training systems for stroke subjects lack an appropriate analytic method, which can achieve a considerable classification accuracy, at the same time detects gradual changes of imagery patterns during rehabilitation process and disinters potential mechanisms about…
In multivariate regression, a -dimensional response vector is regressed upon a common set of covariates, with a matrix of regression coefficients. We study the behavior of the multivariate group Lasso, in which block regularization based on the norm is used for supp…
Mode decomposition is a prototypical pattern recognition problem that can be addressed from the (a priori distinct) perspectives of numerical approximation, statistical inference and deep learning. Could its analysis through these combined perspectives be used as a Rosetta stone for deciphering mechanisms at play in de…
A new distributed algorithm for fitting sparse additive models with feature division and decorrelation.
We solve image inverse problems using a flow-based noise model.
Paper introduces HGSL for heterogeneous graphs, improving edge type and weight recovery.
We derive an arbitrage free relationship between recovery swap rates, digital default swap spreads and conventional CDS spreads, and argue that the fair forward recovery rate used in recovery swaps must contain a convexity premium over the expected recovery value.