arXiv research

Flow Matching for count data improves sample quality and efficiency.

The paper proposes count echo state networks for forecasting graduate student enrollments.

p-SNE embeds Poisson count data into low dimensions preserving structure.

Novel Bayesian method for high-dimensional count data prediction.

Count data take on non-negative integer values and are challenging to properly analyze using standard linear-Gaussian methods such as linear regression and principal components analysis. Generalized linear models enable direct modeling of counts in a regression context using distributions such as the Poisson and negati…

Better neural arithmetic logic units improve cell counting model generalization.

Panel count data describes aggregated counts of recurrent events observed at discrete time points. To understand dynamics of health behaviors, the field of quantitative behavioral research has evolved to increasingly rely upon panel count data collected via multiple self reports, for example, about frequencies of smoki…

The paper improves count data regression models for overdispersed data.

Generative model identifies temporal count data components with regime-dependent contributions.

Multivariate count data are defined as the number of items of different categories issued from sampling within a population, which individuals are grouped into categories. The analysis of multivariate count data is a recurrent and crucial issue in numerous modelling problems, particularly in the fields of biology and e…

This work refines Cover's theory for binary classification on low-dimensional data.

Proposes a method to handle sparse multiway count data with false zeros using zero-truncated Poisson regression.

ENTED efficiently decomposes binary and count tensors using nonparametric Gaussian processes.

Bayesian model tackles spatial count data issues with flexible non-parametric techniques.

Graphical estimation of count time series dependencies.

Counting the number of clusters, when these clusters overlap significantly is a challenging problem in machine learning. We argue that a purely mathematical quantum theory, formulated using the path integral technique, when applied to non-physics modeling leads to non-physics quantum theories that are statistical in na…

New method improves mHealth user engagement using Thompson sampling for count data.

ZICO learns DAGs from zero-inflated count data efficiently.

New phases identified in neural scaling laws with compute limits.

We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their context such that the counts can be aggregated as a stream. We demonstrate performance and scalability of the resulting approach on random quer…

We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts. Panel count data frequently arise when experimental subjects are observed only at discrete time points and only the numbers of occurrences of the events between subsequent observati…

Warped DLMs improve forecasting for count time series.

Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions. Estimating the uncertainty in the measurement is thus vital to making definite, informed conclusions. In this paper, we first compare a range of existing methods to perform counting in medical imaging and…

Count data, for example the number of observed cases of a disease in a city, often arise in the fields of healthcare analytics and epidemiology. In this paper, we consider performing regression on multivariate data in which our outcome is a count. Specifically, we derive log-likelihood functions for finite mixtures of …

This paper proposes learning to jump for generative modeling of sparse, skewed, heavy-tailed data.

MaxSketch improves distinct counting in high-dimensional, noisy data streams.

Semiparametric STAR model improves mental health data analysis.

New method identifies causal structure in count data using cumulants and path analysis.

Transformer learns to estimate negative binomial parameters efficiently.

Clustering with variable selection is a challenging yet critical task for modern small-n-large-p data. Existing methods based on sparse Gaussian mixture models or sparse K-means provide solutions to continuous data. With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current pr…

A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors. The model builds a novel Markov chain that sends the latent gamma random variables at time $(t-1)$ as the shape parameters of those at time $t$, which are linked …

We tackle anomaly detection in sparse time series data.

Counting tripods on a flat torus using lattice point counting.

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting is NP-complete and requires more global inference to oversee the whole graph. To make it …

The ability to detect and count certain substructures in graphs is important for solving many tasks on graph-structured data, especially in the contexts of computational chemistry and biology as well as social network analysis. Inspired by this, we propose to study the expressive power of graph neural networks (GNNs) v…

Proposes PSCCA for estimating correlations and canonical correlations in sparse count data.

Tree-based variational inference improves PLN model for hierarchical count data.

Proposes a new model to better handle overdispersed count time series.

Counting essential surfaces in 3-manifolds yields concise formulae and detailed asymptotics.

Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS). However, translating NGS data faces several challenges. First, NGS count data are often overdispersed, requiring appropriate modeling. Se…

Bayesian model fuses diverse microbiome data types.

Non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson-Gamma models can derive full posterior distributions of latent factors and are less sensitive to sparse count data. However, current inference methods for these Bayesian models adopt restricted update rules f…

Counting geodesics on compact symmetric spaces using orbit dimensions and topological data.

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarchical latent structure from the data, but also capturing both first-order and long-range temporal dependencies. Using sophisticated but simp…

Enhances count process modelling with Markov-modulated non-homogeneous Poisson process.

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statis…

The paper models and predicts co-occurrence counts using Gamma regression.

Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usually result in model overfitting. Besides, current models assume spike counts to be Poisson-distributed, which ignores the fact that many neur…