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.
Detects systematic anomalies in consumer complaints using NLP.
A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be…
The online environment has provided a great opportunity for insurance policyholders to share their complaints with respect to different services. These complaints can reveal valuable information for insurance companies who seek to improve their services; however, analyzing a huge number of online complaints is a compli…
Every year, thousands of people receive consumer product related injuries. Research indicates that online customer reviews can be processed to autonomously identify product safety issues. Early identification of safety issues can lead to earlier recalls, and thus fewer injuries and deaths. A dataset of product reviews …
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…
A variety of methods existing for generating synthetic electronic health records (EHRs), but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness or progress notes. Here, we use the encoder-decoder model, a deep learning algorithm that feature…
New model considers unfairness complaints to ensure multiple fairness criteria.
Language models learn automotive complaints, improving defect detection.
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse…
Many methods have been proposed for detecting emerging events in text streams using topic modeling. However, these methods have shortcomings that make them unsuitable for rapid detection of locally emerging events on massive text streams. We describe Spatially Compact Semantic Scan (SCSS) that has been developed specif…
We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context. For clustering points with centers, we define fairness as proportionality to mean that any points are entitled to form their own cluster if there is another center that is clo…
Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual …
This study examines how fashion consumption affects self-confidence and buying behavior in Iranian consumers.
Analyzes new economic paradigm for non-independent consumer choices.
Method detects multi-timescale consumer spending patterns from receipts.
Study uses EEG and ML to predict movie ratings with 72% accuracy.
Study evaluates deep learning methods for dermatology, finding they perform poorly under non-ideal conditions.
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatica…
Consumer Demand Response (DR) is an important research and industry problem, which seeks to categorize, predict and modify consumer's energy consumption. Unfortunately, traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it diff…
Study finds consumers are more price-sensitive before livestreams than after.
A new ride-hailing subsidy system uses deep causal networks to estimate consumer elasticity.
CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.
In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfor…
New method learns credit prices offline without interaction.
Paper proposes a method to estimate consumer valuations from bundle sales data.
Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which dif…
Study improves retail demand forecasting by integrating macroeconomic data.
Paper uses GANs to simulate consumer transactions with SKU constraints.
System designs for analyzing and pricing non-performing consumer credit portfolios.
GBS uses machine learning to design products based on consumer preferences.
Mapping and translating professional but arcane clinical jargons to consumer language is essential to improve the patient-clinician communication. Researchers have used the existing biomedical ontologies and consumer health vocabulary dictionary to translate between the languages. However, such approaches are limited b…
Proposes a model to optimize feedback for content creators on social media.
Paper explores a consumer-friendly approach to explain machine learning decisions.
Study on self-consuming generative models with diverse human curation, focusing on convergence and stability.
We develop a probabilistic consumer choice framework based on information asymmetry between consumers and firms. This framework makes it possible to study market competition of several firms by both quality and price of their products. We find Nash market equilibria and other optimal strategies in various situations ra…
Understanding consumption dynamics and its impact on the whole economy and welfare within the present economic crisis is not an easy task. Indeed the level of consumer demand for different goods varies with the prices, consumer incomes and demographic factors. Furthermore crisis may trigger different behaviors which re…
Paper stabilizes generative model training with synthetic data.
New model uses symmetries and scaling laws to predict consumer advertising response.
We study the effects of introducing information inefficiency in a model for a random linear economy with a representative consumer. This is done by considering statistical, instead of classical, economic general equilibria. Employing two different approaches we show that inefficiency increases the consumption set of a …
We discuss the stationary states of a model economy in which heterogeneous adaptive consumers purchase commodity bundles repeatedly from sellers. The system undergoes a transition from an inefficient to an efficient state as the number of consumers increases. In the latter phase, however, price fluctuations may…
There are clear benefits associated with a particular consumer choice for many current markets. For example, as we consider here, some products might carry environmental or `green' benefits. Some consumers might value these benefits while others do not. However, as evidenced by myriad failed attempts of environmental p…
Algorithms optimize fair portfolios for diverse risk-tolerant consumers.
This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her…
Enhanced word embedding creates new consumer-friendly health terms.
Current auto loans converge to super-prime credit despite remaining underwater.
We introduce a fully probabilistic framework of consumer product choice based on quality assessment. It allows us to capture many aspects of marketing such as partial information asymmetry, quality differentiation, and product placement in a supermarket.
New method estimates consumer surplus from randomized pricing data.
BC-Aligner maintains backward compatibility of embeddings after frequent updates.