The study compares prepaid and postpaid mobile phone users and predicts their subscription type.
problem Predicting mobile phone subscription type based on usage and network connections.
method Graph labelling approach using max-flow min-cut algorithms and indirect inference methods.
result Graph labelling approach achieves 87% classification accuracy, outperforming supervised learning methods.
Deep learning predicts customer churn from abstract features.
problem Predicting customer churn in subscription-based companies.
method Unsupervised feature learning using deep neural networks on abstract feature vectors.
result Deep learning achieves excellent churn prediction performance across different companies.
Boosting trees predict Twitch subscriptions from user activity.
problem Predicting Twitch user subscriptions from activity data.
method Used boosting trees and target-encodings for high cardinality categoricals.
result User activity can be better predicted than content alone.
Unified metric SCV models subscription business revenue.
problem Analyzing revenue contribution of subscription businesses.
method Bayesian probabilistic model with exponential decay for churn.
result Exact and approximate closed-form solutions for revenue.
Methodology calculates car insurance premiums for partial damage losses.
problem Estimating premiums for partial damage losses in automobile insurance.
method Used generalized linear models to analyze claim frequency and severity.
result Identified key variables influencing claim frequency and severity.
The paper models SaaS products as insurance, offering new pricing tools.
problem Modeling capped-usage SaaS products with insurance principles.
method Frequency-severity decomposition, premium calculation, Monte Carlo simulations.
result SaaS pricing can be analyzed using insurance actuarial methods.
Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suite…
Bayesian model reduces TV watching data to 11 parameters for churn prediction.
problem Predicting customer churn in telecommunications with high-dimensional data.
method Bayesian hierarchical joint model for time-to-event and count data.
result Model reduces data from thousands to 11 customer-level parameter estimates.
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.
This monograph introduces deep learning models for predicting time-to-event outcomes.
problem Predicting critical events and their timing from time series data.
method Neural networks and deep learning models for survival analysis.
result Improved accuracy in predicting time-to-event outcomes using deep learning.
Winning solution for predicting player churn in a video game.
problem Predicting when players will stop playing a game.
method Long Short-Term Memory (LSTM) approach and conditional inference survival ensemble model.
result Models accurately predicted player churn and were robust to changing business models.
Study optimal bidding strategies for digital ads targeting purchases and health campaigns.
problem Optimizing advertising strategies in digital channels.
method Continuous-time models encoding user behavior and auction mechanisms, semi-explicit formulas for optimal bidding.
result Semi-explicit formulas for optimal value and bidding policy for different types of advertising.
Paper proposes a method to train neural networks incrementally using cloud computing despite disconnections and resource outages.
problem Frequent disconnections and resource outages in cloud computing and local machines hinder deep learning model training.
method Introduces an incremental learning framework that allows continuous training of neural networks even with interruptions.
result Demonstrates that incremental learning can maintain progress and train neural networks effectively despite interruptions.
Method estimates treatment effect bounds in sample selection models.
problem Estimating heterogeneous treatment effects in presence of sample selection.
method Debiased/double machine learning approach for non-linear and high-dimensional confounders.
result Substantially tighter effect bounds for younger users.
CENNSurv models cumulative effects of time-dependent exposures on survival outcomes.
problem Challenges in modeling cumulative effects of time-dependent exposures on survival outcomes.
method CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data.
result CENNSurv reveals multi-year lagged and short-term behavioral shifts in survival outcomes.
The topological classification of gerbes, as principal bundles with the structure group the projective unitary group of a complex Hilbert space, over a topological space H is given by the third cohomology H3(H,Z). When H is a topological group the integral cohomology is often related to a locally co…
LLMs help less-resourced researchers access costly data.
problem Unequal access to costly datasets limits research contributions.
method RAG framework with GPT-4o-mini for automated data collection.
result LLMs can collect CEO pay ratios and CAMs from corporate disclosures with high accuracy and low cost.
Typilus predicts types for Python programs using neural networks.
problem Type inference in dynamically typed languages is challenging.
method Graph neural network model that predicts types by probabilistically reasoning over program structure, names, and patterns.
result Typilus can predict types for 70% of all annotatable symbols and type checks 95% of the predicted types.
ChatGPT models classify financial texts with minimal training.
problem Few-shot text classification in finance with limited labels.
method In-context learning with GPT-3.5 and GPT-4, fine-tuning with SetFit.
result GPT models outperform fine-tuned models with fewer examples.
New models for bandit problems with fidelity rewards are introduced and analyzed.
problem Fidelity rewards in bandit problems to incentivize loyalty.
method Two models (loyalty-points and subscription) for fidelity rewards; stochastic and adversarial settings considered.
result Sublinear regret bounds for some models, worst case lower bounds for others.
System tracks regulatory changes for compliance officers.
problem Struggling to keep up with regulatory changes.
method Fetch announcements, classify importance and applicability.
result Simple hierarchical classification works best.
New measures for prediction validity and consonant plausibility introduced.
problem Challenges in predicting future observations and quantifying prediction uncertainty.
method Introducing Type-2 validity and using consonant plausibility measures and conformal prediction.
result Achieving both Type-1 and Type-2 validity through consonant plausibility measures and conformal prediction.
New model predicts fine-grained types for high-multiplicity entities.
problem Fine-grained entity typing with high type multiplicity.
method Set-prediction approach to high-multiplicity fine-grained typing.
result Model outperforms baselines on Wikipedia-based corpus.
Paper proposes an adaptive modeling approach for row-type dependent predictive analysis in banking.
problem Accurate prediction of diverse row types within a single dataset.
method Adaptive modeling approach, tailored data pre-processing, feature engineering, traditional and ensemble machine learning models.
result All predictive approaches achieve a precision rate of no less than 90% for different row types.
New heuristics for predicting links in multiplex networks.
problem Link prediction in networks with multiple types of connections.
method Proposed a general framework and three families of heuristics.
result Significantly outperformed baseline heuristics for ordinary networks.
GC 2022 challenges real-time trend detection in financial tick data.
problem Efficiently detect trading trends in high-volume financial tick data.
method Real-time complex event processing of tick data, focusing on trend indicators and patterns.
result Participants must build reusable and practical solutions for real-life trading decisions.
Paper improves conversion prediction models for online advertising.
problem Predicting different types of conversions in online advertising.
method Multi-Task Learning with MT-FwFM.
result Improved AUC by 0.74% and 0.84% on two conversion types, and overall AUC by 0.50%.
LambdaNet infers TypeScript types using graph neural networks.
problem Automatic inference of TypeScript type annotations.
method Graph Neural Network for type dependency graph analysis.
result LambdaNet outperforms existing methods by 14%.
Paper uses language models to predict MBTI personality types with high accuracy.
problem Predicting Myers-Briggs personality types from text.
method Fine-tuned BERT model for predicting MBTI types and generating personality-specific language.
result BERT model achieves high accuracy in predicting MBTI types and personality-specific language generation.
TaRP predicts missing relations in KGs using type and instance-level info.
problem Missing relations in KGs.
method Type-augmented relation prediction (TaRP) combining type and instance-level info.
result Significantly better performance on benchmark datasets.
Graph neural network predicts JavaScript types with high accuracy.
problem Automatic code repair for JavaScript programs.
method Graph Neural Network model for token type prediction.
result Achieved above 90% accuracy in token type predictions.
Study predicts onset of type II diabetes using survey data and machine learning.
problem Early diagnosis of type II diabetes from patient data.
method Developed an ensemble classifier using five classification algorithms.
result Ensemble model had an AUC of 0.834, indicating high performance.
Framework predicts stock market using mixed data types.
problem Challenges in predicting stock market with diverse data types.
method Model-independent framework for mixed data types (scalar, compositional, functional).
result Framework effectively predicts stock market opening prices.
Models predict fire and other emergencies in Edmonton.
problem Accurate prediction of emergency events for timely response.
method Data collection, descriptive analysis, feature selection, and negative binomial regression.
result Models perform well, with acceptable prediction errors for weekly and monthly periods.
This paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While common link analysis models are limited to single-type link prediction, we attempt here to capture the correlations among different relation types and…
New algorithm predicts multiple types of outputs with dependencies.
problem Predicting multiple diverse types of outputs.
method Problem transformation method combined with component-wise boosting.
result Sparse and interpretable learning of dependencies between targets.
Study predicts academic achievement using students' support networks.
problem Predicting academic achievement in college students.
method Decision tree and random forest algorithms applied to Ties data.
result Different types of support are important for different demographics and genders.
Deep autoencoder predicts cancer types from DNA methylation patterns.
problem Differentiating cancer types based on DNA methylation states.
method Deep learning system with CpG island state classification and statistical methods.
result Overall Sensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75%.
Method explains machine learning diabetes risk predictions.
problem Lack of interpretability in machine learning models.
method Automatic explanation method for machine learning models.
result 87.4% of patients' predictions explained without accuracy loss.
Multimodal deep learning improves toxicity prediction accuracy.
problem Improving prediction accuracy of chemical compound toxicity.
method Combining multiple neural network types and data representations.
result Significantly better accuracy on a toxicity benchmark.
Study uses synthetic data to estimate credit risk for underbanked consumers in Istanbul.
problem Estimating credit risk for underbanked consumers lacking formal credit records.
method Created synthetic dataset, used retrieval augmented generation, trained CatBoost, LightGBM, and XGBoost models.
result Alternative financial data improves credit risk estimation, raising AUC by 13%.
Unihedge uses HTAX to create unlimited liquidity in prediction markets.
problem Limited liquidity and information incorporation issues in prediction markets.
method Introduces HTAX prediction markets with DPM derivatives and new incentive mechanisms.
result Unlimited liquidity and improved information incorporation in prediction markets.
This paper provides a ML framework for diabetes prediction and care management.
problem Diabetes prediction and care management challenges in real-world healthcare.
method Illustrates a Machine Learning framework for T2DM prediction and risk stratification.
result ML models align with physician's disease management steps.
Paper uses HGNN to predict stock types from relationships and temporal data.
problem Predicting stock types from complex market data.
method Integrates stock relationships and temporal data using HGNN.
result Effective prediction of stock types with HGNN model.
Study predicts Type 2 diabetes with 85% accuracy using machine learning.
problem Early detection of Type 2 diabetes improves patient health outcomes.
method Used machine learning algorithms and a weighted ensemble model on patient data.
result Ensemble model achieved 85% accuracy in predicting Type 2 diabetes.
Establishing unambiguously the existence of speculative bubbles is an on-going controversy complicated by the need of defining a model of fundamental prices. Here, we present a novel empirical method which bypasses all the difficulties of the previous approaches by monitoring external indicators of an anomalously growi…
STNN-DDI predicts drug interactions using substructure-aware neural networks.
problem Predicting drug-drug interactions (DDIs) to avoid side effects in poly-drug treatments.
method Designing a novel Substructure-ware Tensor Neural Network (STNN-DDI) that learns a 3-D tensor of substructure-substructure interactions.
result Significant improvement in AUC, AUPR, Accuracy, and Precision compared to state-of-the-art models.
HATS predicts stock and market index movements using hierarchical graph attention.
problem Accurately predicting stock and market index movements using relational data.
method Hierarchical Graph Attention Network (HATS) selectively aggregates information from different relation types.
result HATS outperforms existing methods in predicting stock and market index movements.