11 citations · 12 across the 6 of their papers we have counts for
7 papers
On the Trade-Off Between Transparency and Security in Adversarial Machine Learning
Lucas Fenaux, Christopher Srinivasa, Florian Kerschbaum
Transparency and security are both central to Responsible AI, but they may conflict in adversarial settings. We investigate the strategic effect of transparency for agents through…
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…
A Solver + Gradient Descent Training Algorithm for Deep Neural Networks
Dhananjay Ashok, Vineel Nagisetty, Christopher Srinivasa +1
We present a novel hybrid algorithm for training Deep Neural Networks that combines the state-of-the-art Gradient Descent (GD) method with a Mixed Integer Linear Programming (MILP)…
Nonlocal optimization of binary neural networks
Amir Khoshaman, Giuseppe Castiglione, Christopher Srinivasa
We explore training Binary Neural Networks (BNNs) as a discrete variable inference problem over a factor graph. We study the behaviour of this conversion in an under-parameterized…
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…
Parity Partition Coding for Sharp Multi-Label Classification
Christopher G. Blake, Giuseppe Castiglione, Christopher Srinivasa +1
The problem of efficiently training and evaluating image classifiers that can distinguish between a large number of object categories is considered. A novel metric, sharpness, is p…