arXiv research

New model uses symmetries and scaling laws to predict consumer advertising response.

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…

Study uses EEG and ML to predict movie ratings with 72% accuracy.

In many countries information on expectations collected through consumer confidence surveys are used in macroeconomic policy formulation. Unfortunately, before doing so, the consistency of responses is often not taken into account, leading to biases creeping in and affecting the reliability of the indices hence created…

Proposes a new method to explain model predictions for consumer recourse.

Paper tackles online learning for DR management with incentives.

CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.

A successful response to climate change needs vast investments in low-carbon research, energy, and sustainable development. Governments can drive research, provide environmental regulation, and accelerate global development, but the necessary low-carbon investments of 2-3% GDP have yet to materialise. A new strategy to…

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.

Dynamic pricing aims to match power supply and demand in an energy transition.

This study compares logistic regression and XGBoost for predicting credit risk.

Paper uses CVAE to simulate tariff impacts on electricity consumption.

Federated learning predicts financial distress across U.S. states without centralizing data.

Model predicts anti-cancer drug responses using gene and molecular data.

Paper proposes an intelligent credit limit management system using causal inference.

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…

LDA-XGB1 balances fairness and accuracy in lending models.

We consider sequential decision making problems for binary classification scenario in which the learner takes an active role in repeatedly selecting samples from the action pool and receives the binary label of the selected alternatives. Our problem is motivated by applications where observations are time consuming and…

The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a new application, tuning a deep neural network is a tedious and time consuming process that is often described as a "dark art". This explains t…

Efficient methods estimate concordance probability for big data.

The purpose of this paper is to identify the immediate and future retailer response to wholesale stockouts. We perform a statistical analysis of historical customer order and delivery data of a local tool wholesaler and distributor, whose customers are retailers, over a period of four years. We investigate the effect o…

The study optimizes free trial lengths to boost subscriptions and consumer loyalty.

Efficient deep learning model classifies fractures from X-rays.

We consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of the consumer population, represented as a point in part-worth space, using an ad…

Machine learning plays an increasing role in intelligent tutoring systems as both the amount of data available and specialization among students grow. Nowadays, these systems are frequently deployed on mobile applications. Users on such mobile education platforms are dynamic, frequently being added, accessing the appli…

New algorithm reduces regret in dynamic assortment selection.

Procedure verifies if machine learning models assign fixed predictions that preclude access.

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…

CAG method predicts nonlinear solid mechanics responses in real-time with high accuracy and efficiency.

Solves TOD systems' query annotation problem without explicit annotations.

The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown t…

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.

Detects systematic anomalies in consumer complaints using NLP.

Estimates causal effect using proxies in multi-domain settings.

A new ride-hailing subsidy system uses deep causal networks to estimate consumer elasticity.

Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes. Recently, a new framework for providing explanations, called TED, has been proposed to provide meaningful explana…

New method learns credit prices offline without interaction.

Paper proposes a method to estimate consumer valuations from bundle sales data.

Study improves retail demand forecasting by integrating macroeconomic data.

Paper uses GANs to simulate consumer transactions with SKU constraints.

Design of experiments improves validation of biomolecular networks.

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.