arXiv research

Model counts event occurrences to detect and locate events in data.

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

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…

A new test statistic counts tree co-occurrences to detect edge correlation between networks.

odeN efficiently approximates multiple temporal motifs in large networks.

Given a stationary point process, an intensity burst is defined as a short time period during which the number of counts is larger than the typical count rate. It might signal a local non-stationarity or the presence of an external perturbation to the system. In this paper we propose a novel procedure for the detection…

Proposes a method for two-sided clustering of co-occurrence data.

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

Exploratory analysis over network data is often limited by the ability to efficiently calculate graph statistics, which can provide a model-free understanding of the macroscopic properties of a network. We introduce a framework for estimating the graphlet count---the number of occurrences of a small subgraph motif (e.g…

Unified framework for comparing clusterings from information-theoretic and pair-counting perspectives.

Continuous vector representations of words and objects appear to carry surprisingly rich semantic content. In this paper, we advance both the conceptual and theoretical understanding of word embeddings in three ways. First, we ground embeddings in semantic spaces studied in cognitive-psychometric literature and introdu…

PHIBP models complex microbiome data with shared parameters.

The study analyzes a model for aggregate losses with dependent and overdispersed inter-losses times.

New model bridges pricing and reserving for insurance claims.

This paper analyzes DeepWalk and node2vec for community detection in large networks.

Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging …

Develops a measure-theoretic framework for complex co-occurrence data.

A new method for higher-order co-occurrences in hypergraphs.

Corrects a mistake in $hv$-block cross-validation consistency claim.

Language models allocate information storage, not collapsing into uniform representations.

A natural occurrence of shift equivalence in a purely algebraic setting constitutes the subject matter of the following short exposition.

Conventional text classification models make a bag-of-words assumption reducing text into word occurrence counts per document. Recent algorithms such as word2vec are capable of learning semantic meaning and similarity between words in an entirely unsupervised manner using a contextual window and doing so much faster th…

Develops a neural model to predict event occurrence and timing.

We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks. We use l0 norm to model sparse errors over discrete values (occurrences), and use decomposed factors to model the embedded groups of nodes. An efficient splitting method is developed to optimize the non…

Advances citation and subject label recommendation using multi-modal adversarial autoencoders.

In this work, we consider hypothesis testing and anomaly detection on datasets where each observation is a weighted network. Examples of such data include brain connectivity networks from fMRI flow data, or word co-occurrence counts for populations of individuals. Current approaches to hypothesis testing for weighted n…

New findings support a new community recovery threshold for Stochastic Block Model with many communities.

Matrix Chernoff bound for Markov chains applied to co-occurrence matrices.

Matrix factorization is a simple and effective solution to the recommendation problem. It has been extensively employed in the industry and has attracted much attention from the academia. However, it is unclear what the low-dimensional matrices represent. We show that matrix factorization can actually be seen as simult…

Representing a word by its co-occurrences with other words in context is an effective way to capture the meaning of the word. However, the theory behind remains a challenge. In this work, taking the example of a word classification task, we give a theoretical analysis of the approaches that represent a word X by a func…

This paper considers the problem of predicting the number of events that have occurred in the past, but which are not yet observed due to a delay. Such delayed events are relevant in predicting the future cost of warranties, pricing maintenance contracts, determining the number of unreported claims in insurance and in …

In lexicon-based classification, documents are assigned labels by comparing the number of words that appear from two opposed lexicons, such as positive and negative sentiment. Creating such words lists is often easier than labeling instances, and they can be debugged by non-experts if classification performance is unsa…

We prove that if $N\ge 2$ and $α: F_N\to π_1(Γ)$ is a marking on $F_N$, then for any integer $r\ge 2$ and any $F_N$-invariant collection of non-negative integral "weights" associated to all subtrees $K$ of $\widetilde Γ$ of radius $\le r$ satisfying some natural "switch" conditions, there exists a finite cyclically red…

Analyzes word vectors and co-occurrence statistics in NLP models.

Various measures can be used to estimate bias or unfairness in a predictor. Previous work has already established that some of these measures are incompatible with each other. Here we show that, when groups differ in prevalence of the predicted event, several intuitive, reasonable measures of fairness (probability of p…

We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary va…

Counting tripods on a flat torus using lattice point counting.

Paper proposes a new method for accurate data labeling using pairwise co-occurrences.

New model captures patient-level EHR data efficiently.

New computational methods improve clustering of objects.

Chromatic Learning reduces feature dimensions for sparse datasets.

Flow Matching for count data improves sample quality and efficiency.

New method reconstructs past foehn occurrences using unsupervised and supervised learning.

Counting orbits for Anosov groups with specific functionals.

Models of bags of words typically assume topic mixing so that the words in a single bag come from a limited number of topics. We show here that many sets of bag of words exhibit a very different pattern of variation than the patterns that are efficiently captured by topic mixing. In many cases, from one bag of words to…

Method estimates exogenous and endogenous factors from event times.

Improved KG completion on large datasets using entity-relation pair occurrences.

New theorem counts curves on orbifolds.