Fairness-aware machine learning has attracted a surge of attention in ma...
Data subsampling is widely used to speed up the training of large-scale
...
Sequential recommender systems aim to predict users' next interested ite...
Counterfactual explanations promote explainability in machine learning m...
Self-attention models have achieved state-of-the-art performance in
sequ...
Fair machine learning aims to mitigate the biases of model predictions
a...
This work considers the out-of-distribution (OOD) prediction problem whe...
Recommendation is one of the critical applications that helps users find...
Online users generate tremendous amounts of textual information by
parti...
With convenient access to observational data, learning individual causal...
The spread of harmful mis-information in social media is a pressing prob...
The overturning of the Internet Privacy Rules by the Federal Communicati...
The era of big data provides researchers with convenient access to copio...
Modeling spillover effects from observational data is an important probl...
High-order parametric models that include terms for feature interactions...