41 citations · 138 across the 11 of their papers we have counts for
5 papers · 1 filter
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya Nori
Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a d…
Robustness of Neural Networks: A Probabilistic and Practical Approach
Ravi Mangal, Aditya V. Nori, Alessandro Orso
Neural networks are becoming increasingly prevalent in software, and it is therefore important to be able to verify their behavior. Because verifying the correctness of neural netw…
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc +6
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to…
Measuring Neural Net Robustness with Constraints
Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos +3
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled. We pr…
Debugging Machine Learning Tasks
Aleksandar Chakarov, Aditya Nori, Sriram Rajamani +2
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of co…