GitHub

3rd Workshop on Causal Inference and Machine Learning in Practice

The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal ...
VentureBeat

New ‘Test-Time Training’ method lets AI keep learning without exploding inference costs

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...
GitHub

mohansree14/Bayesian-Deep-Learning-for-Probabilistic-Financial-Forecasting

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
ascopubs.org

Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...
Scientific Research Publishing

Hill, J.L. (2011) Bayesian Nonparametric Modeling for Causal Inference.

ABSTRACT: This study investigates the persistent academic impacts of the Head Start program, a federal government-funded early childhood intervention, using data from the Early Childhood Longitudinal ...
Scientific Research Publishing

A Systems-Based Balanced Cost-Benefit Analysis of Corporate-Community Engagement in Resource Extraction: The CoSLIE Framework ()

This study develops and empirically calibrates the Community-Social Licence-Insurance Equilibrium (CoSLIE) Model, a dynamic, multi-theoretic framework that reconceptualises corporate-community ...
IEEE

A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines

Abstract: Predictive maintenance is essential for ensuring the reliability and efficiency of wind energy systems. Traditional deep learning models for sensor fault detection rely solely on data-driven ...
IEEE

Bayesian Inference for Transformer Degradation Prediction: A Comparative Analysis with Traditional and Machine Learning Models

Abstract: Predicting transformer degradation is essential for ensuring the reliability and efficiency of power systems. This study explores Bayesian inference as a robust alternative to traditional ...