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.
Bayesian model improves categorization of explosions from sparse data.
Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem…
Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process. In many cases, such events can be described as a collection of categorical values that are considered as entities of different types, which we call heterogeneous categor…
We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models. A data set of temporal event sequences can be represented as a data set of three-dimensional points, each point is defined by three varia…
cGAP visualizes high-dimensional categorical data with interpretable geometric structure.
cGAP visualizes high-dimensional categorical data with interpretable geometric structure.
VAIOM models financial returns using continuous input and categorical output.
Method reduces categorical data to lower dimensions using density matrices.
Embedded markup of Web pages has seen widespread adoption throughout the past years driven by standards such as RDFa and Microdata and initiatives such as schema.org, where recent studies show an adoption by 39% of all Web pages already in 2016. While this constitutes an important information source for tasks such as W…
Modeling solar ramping events with spatio-temporal point processes.
We propose a generalization of quantization as a categorical way. For a fixed Poisson algebra quantization categories are defined as subcategories of R-module category with the structure of classical limits. We construct the generalized quantization categories including matrix regularization, strict deformation quantiz…
A very simple event frequency approximation algorithm that is sensitive to event timeliness is suggested. The algorithm iteratively updates categorical click-distribution, producing (path of) a random walk on a standard -dimensional simplex. Under certain conditions, this random walk is self-similar and corresponds …
The paper proposes a method to analyze categorical feature interactions in large datasets using graph covariance and LLMs.
We consider the problem of completing a matrix with categorical-valued entries from partial observations. This is achieved by extending the formulation and theory of one-bit matrix completion. We recover a low-rank matrix by maximizing the likelihood ratio with a constraint on the nuclear norm of , and the obser…
Survey on modeling event sequences through temporal processes.
New STH distance finds patterns in event timeseries without resampling.
A new model estimates mixed memberships for categorical data with weighted responses.
New algorithms for latent class analysis using regularized spectral clustering.
Discusses handling intercurrent events in clinical trials with time-to-event outcomes.
New model for multivariate discrete event data with flexible interactions.
We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix map…
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventiv…
Applied Data Scientists throughout various industries are commonly faced with the challenging task of encoding high-cardinality categorical features into digestible inputs for machine learning algorithms. This paper describes a Bayesian encoding technique developed for WeWork's lead scoring engine which outputs the pro…
DPERC efficiently estimates covariance matrices for mixed data with missing values.
Paper introduces Categorical Normalizing Flows for better handling of categorical data.
CEDA analyzes large categorical datasets using tree geometry and binary codes.
A new method for Gaussian Processes handles mixed continuous and categorical inputs.
Efficient event generation for collider phenomenology using parallel Langevin sampling and learned Stein diagnostics.
New GP kernel handles mixed-categorical data, improving model accuracy.
A new catnat function improves gradient descent for categorical variables.
Method estimates exogenous and endogenous factors from event times.
WE constructs GP kernels for mixed inputs using weighted EDMs.
Cluster analysis is one of the essential tasks in data mining and knowledge discovery. Each type of data poses unique challenges in achieving relatively efficient partitioning of the data into homogeneous groups. While the algorithms for numeric data are relatively well studied in the literature, there are still challe…
The episodic, irregular and asynchronous nature of medical data render them difficult substrates for standard machine learning algorithms. We would like to abstract away this difficulty for the class of time-stamped categorical variables (or events) by modeling them as a renewal process and inferring a probability dens…
DynForest R package predicts outcomes with time-dependent predictors.
We introduce a new non parametric method that allows for a direct, fast and efficient estimation of the matrix of kernel norms of a multivariate Hawkes process, also called branching ratio matrix. We demonstrate the capabilities of this method by applying it to high-frequency order book data from the EUREX exchange. We…
Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We consider several class-agnostic semantic constraints that apply to unlabeled nonspeech audio: (i) noise and translations in time do not change t…
Study uses LLMs to categorize financial tweets, revealing useful sentiment signals.
It is shown, that the mapping class group of a surface of the genus g > 1 admits a faithful representation into the matrix group GL (6g-6, Z). The proof is based on a categorical correspondence between the Riemann surfaces and the so-called toric AF-algebras.
In this paper, we consider the problem of event classification with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex temporal dependencies between the variables combined with sparsity of the data makes the event classification problem particularly challengin…
We present a neural network for predicting purchasing intent in an Ecommerce setting. Our main contribution is to address the significant investment in feature engineering that is usually associated with state-of-the-art methods such as Gradient Boosted Machines. We use trainable vector spaces to model varied, semi-str…
PRESTO improves rare event prediction by shrinking towards proportional odds model.
This paper models how features influence event triggers in high-dimensional networks.
VOWEL trains WTA-SNNs for multi-valued events, overcoming resource limitations.
New model for clustering dependent community Hawkes processes in temporal networks.
A new method for embedding sparse high-order interactions.
Categorical regressor variables are usually handled by introducing a set of indicator variables, and imposing a linear constraint to ensure identifiability in the presence of an intercept, or equivalently, using one of various coding schemes. As proposed in Yuan and Lin [J. R. Statist. Soc. B, 68 (2006), 49-67], the gr…
Paper proposes GANs for generating business process suffixes and remaining times.