11 citations · 14 across the 5 of their papers we have counts for
10 papers
fAux: Testing Individual Fairness via Gradient Alignment
Giuseppe Castiglione, Ga Wu, Christopher Srinivasa +1
Machine learning models are vulnerable to biases that result in unfair treatment of individuals from different populations. Recent work that aims to test a model's fairness at the…
Scalable Whitebox Attacks on Tree-based Models
Giuseppe Castiglione, Gavin Ding, Masoud Hashemi +2
Adversarial robustness is one of the essential safety criteria for guaranteeing the reliability of machine learning models. While various adversarial robustness testing approaches…
PUMA: Performance Unchanged Model Augmentation for Training Data Removal
Ga Wu, Masoud Hashemi, Christopher Srinivasa
Preserving the performance of a trained model while removing unique characteristics of marked training data points is challenging. Recent research usually suggests retraining a mod…
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction
Yi Sui, Ga Wu, Scott Sanner
Spatiotemporal prediction of event data is a challenging task with a long history of research. While recent work in spatiotemporal prediction has leveraged deep sequential models t…
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering
Zheda Mai, Ga Wu, Kai Luo +1
Most existing One-Class Collaborative Filtering (OC-CF) algorithms estimate a user's preference as a latent vector by encoding their historical interactions. However, users often s…
Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering
Jin Peng Zhou, Ga Wu, Zheda Mai +1
One-class collaborative filtering (OC-CF) is a common class of recommendation problem where only the positive class is explicitly observed (e.g., purchases, clicks). Autoencoder ba…