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.
Proposes a new signal model for high-dimensional, small-sample-size data.
Paper improves signal proportion estimation by accounting for variable dependence.
We propose a Bayesian expectation-maximization (EM) algorithm for reconstructing Markov-tree sparse signals via belief propagation. The measurements follow an underdetermined linear model where the regression-coefficient vector is the sum of an unknown approximately sparse signal and a zero-mean white Gaussian noise wi…
Develops a Bayesian non-parametric approach for signal separation with varying components.
Better signal detection in undersampled data using joint and cross covariances.
Causal inference concerns the identification of cause-effect relationships between variables, e.g. establishing whether a stimulus affects activity in a certain brain region. The observed variables themselves often do not constitute meaningful causal variables, however, and linear combinations need to be considered. In…
VPNet uses variable projection for efficient neural network training.
Optimizes signal detection in particle physics by decorrelating classifiers.
We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using sparsity optimization…
Optimizes PnL using linear signals in quantitative finance.
The paper develops a method to model high-dimensional data with many variables and weak signals.
New AMP algorithm estimates signals and latent variables in mixed regression models.
A new method for joint noise removal and trend estimation from sparse signals.
We derive fundamental sample complexity bounds for recovering sparse and structured signals for linear and nonlinear observation models including sparse regression, group testing, multivariate regression and problems with missing features. In general, sparse signal processing problems can be characterized in terms of t…
CardiacGen generates realistic ECG signals for training deep learning models.
This paper studies ordered weighted L1 (OWL) norm regularization for sparse estimation problems with strongly correlated variables. We prove sufficient conditions for clustering based on the correlation/colinearity of variables using the OWL norm, of which the so-called OSCAR is a particular case. Our results extend pr…
Brain signal variability in the measurements obtained from different subjects during different sessions significantly deteriorates the accuracy of most brain-computer interface (BCI) systems. Moreover these variabilities, also known as inter-subject or inter-session variabilities, require lengthy calibration sessions b…
A new framework converts EEG signals between subjects and tasks.
Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a generative model. We search and constrain on latent variable space to make the m…
This paper extends compositional data analysis using graph signal processing.
The pattern theory of Grenander is a mathematical framework where patterns are represented by probability models on random variables of algebraic structures. In this paper, we review three families of probability models, namely, the discriminative models, the descriptive models, and the generative models. A discriminat…
Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers for signals observed at constant and variable intervals.
MultiImport infers node importance from multiple KG signals.
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal. Therefore, (1) explaining away the structured noise in multiple-output regression is of paramount importanc…
PolarBM models complex-valued audio signals in polar coordinates, improving over conventional methods.
I introduce a general, Bayesian method for modelling univariate time series data assumed to be drawn from a continuous, stochastic process. The method accommodates arbitrary temporal sampling, and takes into account measurement uncertainties for arbitrary error models (not just Gaussian) on both the time and signal var…
Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals reco…
Unified framework for learning with indirect supervision signals.
We find ways to make physical signals misclassified by computer vision models.
LatentNN corrects neural network attenuation bias in astronomical data.
Sharp-SSL uses random projections to identify important variables for semi-supervised learning.
Signals are geometric submanifolds with specific properties.
Paper introduces rational Gaussian wavelets for efficient signal approximation.
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal fro…
Improved robust latent variable estimation for neural dynamics.
Algorithm estimates common mean from Gaussian variables with unknown variances.
New method uses sufficient statistics to infer causal relationships from observational data.
We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for dif…
Develops methods to estimate high rank tensors from noisy data.
Solar algorithm selects variables faster and more accurately in high-dimensional data.
We analyze the performance of alternating minimization for loss functions optimized over two variables, where each variable may be restricted to lie in some potentially nonconvex constraint set. This type of setting arises naturally in high-dimensional statistics and signal processing, where the variables often reflect…
New sampler reduces MCMC complexity for Bayesian variable selection.
Enhances financial data signal-to-noise ratio using auto-encoders and mutual regularization.
This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieve…
Paper proposes a deep RL method for hedging variable annuities, outperforming misspecified models.
High throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data…
The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not always readily available from the data, nor easy to define depending…
Graph-Dictionary model for sparse multivariate signal representation.