A framework for faster, better infographic design by non-experts and experts alike.
problem Designing infographics is time-consuming and tedious for non-experts and even professionals.
method Semi-automated infographic framework for structured and flow-based designs, including automatic design ranking and customization options.
result Designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining quality.
Automated neural network synthesis engine for custom designs.
problem Difficulty in creating custom neural networks due to lack of skills and time.
method Automated synthesis engine NeuNetS for text and image domains.
result Builds neural networks faster and with similar accuracy to human-designed models.
Improved resource allocation method reduces procurement costs.
problem Online resource allocation with procurement costs.
method Primal-dual algorithm with surrogate function optimization.
result Enhanced competitive ratio through design methods.
Demand response is designed to motivate electricity customers to modify their loads at critical time periods. The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program. In practice, learning these response is nontrivial because operators can only send a …
Study clusters bank customers using LSTM and DTW.
problem Efficiently segmenting bank customers for targeted offers.
method Encoder-decoder LSTM network and Dynamic Time Warping (DTW).
result Hybrid method yields more accurate clusters.
Proposes a variational autoencoder for long-term customer revenue forecasting.
problem Predicting long-term customer revenue from sparse and irregular transaction data.
method Variational Autoencoder (VAE) with flexible latent representation.
result Improves upon latest benchmarks in multiple real-world datasets.
fintech-kMC simulates financial platforms for AI/ML model validation.
problem Validation of AI/ML models in real-world financial applications.
method Agent-based model with kinetic Monte Carlo engine.
result Generates realistic synthetic data for testing AI/ML models.
Study shows non-systematic bias in customer satisfaction surveys limits data value.
problem Non-systematic bias in customer satisfaction surveys limits data value.
method Used real customer satisfaction survey data of a large retail bank to show the irreducible error and suggest thoughtful survey design methods.
result A thoughtful survey design can reduce non-systematic error in customer satisfaction surveys.
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention dis…
This study designs a financial risk control platform using big data and machine learning.
problem Traditional risk management models are inadequate for modern financial complexities.
method Big data mining, real-time streaming data processing, statistical analysis, and precise customer behavior mining.
result The platform effectively identifies and responds to potential risks in real-time.
Comparative study of neural networks for short-term FOREX forecasting.
problem Simulating expert judgment in foreign exchange market forecasting.
method Implemented and compared LSTM and ANN architectures for short-term FOREX forecasting.
result ANN custom architecture outperforms LSTM in prediction quality and resource efficiency.
The study optimizes free trial lengths to boost subscriptions and consumer loyalty.
problem Optimizing free trial lengths to maximize customer acquisition and retention.
method A large-scale field experiment with personalized policy design and evaluation.
result Personalized free trial policies outperform uniform trial lengths.
E-commerce system predicts customer interest in sequentially ordered products.
problem Modeling customer interest in sequentially ordered products.
method Embed purchased items with Word2Vec and model the sequence with LSTM RNN.
result The recommender system outperforms its predecessor in click-through rate.
MiniHack simplifies creation of complex RL environments.
problem Limited availability of challenging RL benchmarks.
method Develops a sandbox framework for easy RL environment design.
result MiniHack enables rapid creation of diverse RL testbeds.
Method learns equivariances from data without custom architecture design.
problem Learning equivariances for tasks without manually designed architectures.
method Reparameterization to learn equivariant parameter sharing.
result Can learn equivariances for any finite group of transformations.
LLM extracts actionable insights from customer reviews.
problem Extracting actionable insights from customer reviews.
method Large language model approach distinguishing perceptual attributes from actionable features.
result High consistency and predictive validity of LLM insights compared to human coders.
The paper develops a reinforcement learning model to estimate ad impact considering delayed and cumulative effects.
problem Accurately estimating ad impact considering delayed and long-term effects, cumulative impacts, and customer heterogeneity.
method Modeling ad bidding as a Contextual Markov Decision Process (CMDP) with delayed Poisson rewards, proposing a two-stage maximum likelihood estimator and reinforcement learning algorithm.
result Achieves a near-optimal regret bound of O ~ ( d H 2 T ) \tilde{O}{(dH^2\sqrt{T})} O ~ ( d H 2 T ) , validating the approach through simulation experiments. In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---probabilistic graphical models and large margin methods---have their own distinct strengths but also possess significant drawbacks. Conditio…
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benefits of TensorFlow including automatic differentiation, parallelization and GPU computations for Bayesian optimization. Design goals focus on…
Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely …
Automates GNN design for molecular property prediction.
problem Designing and tuning GNN architectures for molecular property prediction is labor-intensive.
method Developed a NAS approach to automatically discover high-performing GNN architectures for MPNNs.
result Automatically discovered MPNNs outperform manually designed GNNs in molecular property prediction.
The purpose of this study was to build a customer selection model based on 20 dimensions, including customer codes, total contribution, assets, deposit, profit, profit rate, trading volume, trading amount, turnover rate, order amount, withdraw amount, withdraw rate, process fee, process fee submitted, process fee retai…
Improved stock price prediction using LSTM with customized loss function and analyst calls.
problem Forecasting stock prices using LSTM neural networks.
method Customized LSTM model with improved loss function, analyst calls integration, and attention units.
result Improved performance of LSTM model over ARIMA for stock price prediction.
DCE learns customer embeddings from digital activity and financial context.
problem Comprehensive customer understanding in financial services.
method Leverages customers' digital activity and financial context to learn dense representations.
result DCE showed performance lift in three prediction problems.
BNNpriors library improves Bayesian neural network inference with various prior distributions.
problem Challenges in choosing good prior distributions for Bayesian neural networks.
method State-of-the-art Markov Chain Monte Carlo inference with a wide range of predefined priors.
result Facilitates foundational discoveries on the nature of the cold posterior effect.
Reduce survey questions to scale market research without annoying customers.
problem Performing market research by surveying customers with many questions is inefficient and annoying.
method Used Bayesian networks to model and reduce the number of questions asked to customers.
result Demonstrated the effectiveness of the approach using an example of broadband customer segmentation.
Major advancements in building general-purpose and customized hardware have been one of the key enablers of versatility and pervasiveness of machine learning models such as deep neural networks. To sustain this ubiquitous deployment of machine learning models and cope with their computational and storage complexity, se…
Financial institutions use LSTM models to predict customer goals.
problem Predicting customer goals and actions in financial services.
method Used LSTM models with state-space graph embeddings on historical customer traces.
result Demonstrated the effectiveness of LSTM models in predicting customer goals and actions.
Customer momentum is a positive relationship between a firm's returns and past returns of its customers.
problem Understanding the relationship between a firm's returns and its customers' past returns.
method Examined customer momentum using a long-short equally-weighted decile portfolio and Fama-French factor models.
result Customer momentum generates significant monthly returns and is statistically significant.
With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency. In this work, we explore unconventional narrow-precision floating-point representations as it relates …
Method generates realistic customer transaction sequences for retail analysis.
problem Modeling customer behavior and purchasing patterns in retail databases.
method Customer embedding using RNN and GAN for generating plausible baskets.
result Generated baskets resemble real transactions and replicate sequential patterns.
Behavior modification improves prediction accuracy by nudging user behavior.
problem Improving prediction accuracy using behavior modification techniques.
method Combining prediction and behavior modification with reinforcement learning algorithms.
result Behavior modification can make predictions more certain but may not generalize.
PASTA optimizes assortment selection using pessimism principle.
problem Optimizing assortment selection with limited data coverage.
method Pessimistic Assortment Optimization (PASTA) based on the principle of pessimism.
result PASTA correctly identifies optimal assortment with minimal data coverage.
Paper proposes a new topology for AML analysis using Poincaré embeddings.
problem Complex money laundering schemes and regulatory constraints hinder AML analysis and information sharing.
method Proposes a new topology for AML analysis using Poincaré embeddings.
result Demonstrates improved AML analysis and information sharing through Poincaré embeddings.
DAD learns to design experiments quickly, outperforming traditional methods.
problem Real-time decision-making in sequential Bayesian experimental design.
method Amortized design network trained with contrastive information bounds.
result DAD outperforms alternative strategies on various problems.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
problem Cold start problem and under-representation of new or under-impressed products in e-commerce search results.
method Aggregates customer engagements within a day for the same query as input training data for machine learning models.
result Training models on aggregated data leads to better ranking of new and under-impressed products.
Study uses Open Banking data to estimate customer value, showing potential 21% increase.
problem Limited CLV estimation using single-entity data.
method Introduces PCLV framework using Open Banking data for comprehensive customer value estimation.
result Open Banking data can estimate PCLV per competitor, showing a 21.06% increase over Actual CLV.
Ancestry improves genealogy search results by ranking diverse record types.
problem Ranking diverse genealogy records equitably from various sources.
method Customized Coordinate Ascent, Stochastic Search, Normalized Cumulative Entropy.
result Demonstrated effectiveness of algorithms in improving relevance and diversity.
Predicts customer call intent for auto dealerships using CNN.
problem Understanding customer intent from phone calls for better service.
method Developed a CNN-based supervised learning model for multi-class classification.
result CNN model performs well on customer call intent classification.
Proposes robust assortment optimization from observational data.
problem Real-world scenarios often violate assumptions of stable customer preferences and correct choice models.
method Develops a robust framework that accounts for potential distributional shifts in customer choice behavior.
result Uncovered the notion of ``robust item-wise coverage'' as the minimal data requirement for sample-efficient robust assortment learning.
VAE learns latent representations for bank customers' creditworthiness.
problem Improving marketing, CRM, and credit risk assessment in retail banks.
method Adopted Variational Autoencoder (VAE) to learn latent representations.
result VAE latent representations capture customers' creditworthiness and generalize to new data.
Adaptive framework improves airline pricing models' performance.
problem No single model dominates other models for all customer requests.
method Adaptive meta-decision framework using Thompson sampling.
result Improves expected revenue per offer by 43% and conversion score by 58%.
The paper uses RFM and clustering to segment bank customers.
problem Challenges in customer retention and profitable segmentation in banking.
method RFM technique and clustering algorithms applied to real data.
result Successful customer segmentation improves conversion rates.
The study improves CLV predictions in retail banking with machine learning.
problem Estimating customer lifetime value in retail banking is challenging.
method Developed a novel framework for CLV predictions over arbitrary time horizons.
result 43% improvement in out-of-time CLV prediction error.
In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs). In particular, we demonstrate our method in controlling color, texture, and shape of a generated garment image for computer-aided fashion design. To disentangle the effect of input at…
Study models growth of unorganized retail in Indian pharma sector amid organized and e-retail competition.
problem Survival strategies of unorganized retailers challenged by organized and e-retailers.
method Field investigation, conjoint analysis, design of experiments, agent-based simulation.
result Unorganized retailers' performance influenced by their own attribute levels and price discounts.
Optimized neural networks for Edge TPU achieve high accuracy in real-time image classification.
problem Designing neural networks for hardware accelerators to achieve optimal performance.
method Hardware-aware neural architecture search and model customization for Edge TPU.
result Improved accuracy-latency tradeoff on Pixel 4's Edge TPU compared to existing models.
Study compares classification techniques to predict customer churn in banking.
problem Predicting customer churn in banking industry.
method Comparison of six supervised classification techniques (ANN and random forest) on 10000 European bank customers.
result ANN structure with five nodes in a single hidden layer is the best performing classifier.