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.
Efficiently models correlated topics with topic embeddings.
Recent advances in topic models have explored complicated structured distributions to represent topic correlation. For example, the pachinko allocation model (PAM) captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). While PAM provides more flexibility and gr…
We introduce Gaussian Process Topic Models (GPTMs), a new family of topic models which can leverage a kernel among documents while extracting correlated topics. GPTMs can be considered a systematic generalization of the Correlated Topic Models (CTMs) using ideas from Gaussian Process (GP) based embedding. Since GPTMs w…
New metric correlates local topic quality with human judgments.
A scalable topic model for large document collections using MapReduce.
The logistic normal distribution has recently been adapted via the transformation of multivariate Gaus- sian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. O…
TopicEq model generates equations and text from scientific papers.
While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce …
FACTM combines FA with correlated topic modeling for structured data integration.
Many predictive tasks, such as diagnosing a patient based on their medical chart, are ultimately defined by the decisions of human experts. Unfortunately, encoding experts' knowledge is often time consuming and expensive. We propose a simple way to use fuzzy and informal knowledge from experts to guide discovery of int…
Multi-modal data collections, such as corpora of paired images and text snippets, require analysis methods beyond single-view component and topic models. For continuous observations the current dominant approach is based on extensions of canonical correlation analysis, factorizing the variation into components shared b…
Topic-aware chatbot learns from NMF topic vectors.
In this paper, we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the limitation of LDA to incorporate arbitrary topic correlations, by assuming that the hidden topic proportions are drawn from a flexible class o…
CFTM uses fractional Brownian motion for dynamic topic modeling.
A new model analyzes document structure and customer shopping patterns.
Study uses topic modeling and sentiment analysis to uncover hedge fund performance insights.
We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN…
Unsupervised estimation of latent variable models is a fundamental problem central to numerous applications of machine learning and statistics. This work presents a principled approach for estimating broad classes of such models, including probabilistic topic models and latent linear Bayesian networks, using only secon…
Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropria…
TSPRA integrates topics, sentiment, and user preference for better online review prediction and analysis.
We present an outlook of the studies on correlations in the price timeseries of stocks, discussing the construction and applications of "asset tree". The topic discussed here should illustrate how the complex economic system (financial market) enrichens the list of existing dynamical systems that physicists have been s…
In topic modeling, many algorithms that guarantee identifiability of the topics have been developed under the premise that there exist anchor words -- i.e., words that only appear (with positive probability) in one topic. Follow-up work has resorted to three or higher-order statistics of the data corpus to relax the an…
Overcomplete latent representations have been very popular for unsupervised feature learning in recent years. In this paper, we specify which overcomplete models can be identified given observable moments of a certain order. We consider probabilistic admixture or topic models in the overcomplete regime, where the numbe…
Twitmo analyzes geo-tagged Twitter data for topic modeling and visualization.
Paper presents efficient variational inference for Pachinko Allocation Machines.
Proposes a new method for evaluating and constructing hierarchical topic models.
Geometric QHD tests improve hub detection in correlated data.
Predicting market volatility from financial news and tweets.
Paper develops a framework for generating coherent image captions using visual features and hierarchical topics.
The problem of topic modeling can be seen as a generalization of the clustering problem, in that it posits that observations are generated due to multiple latent factors (e.g., the words in each document are generated as a mixture of several active topics, as opposed to just one). This increased representational power …
LDTA expands LDA's topic modeling capacity with tree-structured priors.
Non-convex optimization problems often arise from probabilistic modeling, such as estimation of posterior distributions. Non-convexity makes the problems intractable, and poses various obstacles for us to design efficient algorithms. In this work, we attack non-convexity by first introducing the concept of \emph{probab…
Deriving the optimal safety stock quantity with which to meet customer satisfaction is one of the most important topics in stock management. However, it is difficult to control the stock management of correlated marketable merchandise when using an inventory control method that was developed under the assumption that t…
Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate, the coordinate updates are easily …
GRASP removes spurious correlations in fine-tuned models, improving task performance and reducing bias.
CATVI improves variational inference for Bayesian nonparametric models by reducing divergence and improving prediction accuracy.
Bayesian Topic Regression models causal inference with text and numerical data.
Enhanced metrics for multiclass classification improve on existing methods.
Research analyzes public opinions on Twitter about diabetes, diet, exercise, and obesity.
This paper benchmarks Bayesian models' ability to estimate predictive correlations, especially for active learning.
Paper proposes a new stock price forecasting method using DRAGAN and feature matching.
This paper uses rank correlation methods to construct MSTs from financial returns, finding them more stable and robust.
Paper forecasts corporate default risk using Particle MCMC with expert opinions.
We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier new…
Paper proposes a new portfolio model for better investment decisions.
Analyzes stock trends and e-commerce user behavior using Twitter data.
Unified framework for interpreting complex regression models with many predictors.
A new memory strategy extends BPTT's learning window without longer traces.