41 citations · 138 across the 11 of their papers we have counts for
13 papers
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran +6
We present CRYPTFLOW, a system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build two compone…
Towards Compliant Data Management Systems for Healthcare ML
Goutham Ramakrishnan, Aditya Nori, Hannah Murfet +1
The increasing popularity of machine learning approaches and the rising awareness of data protection and data privacy presents an opportunity to build truly secure and trustworthy…
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…
Overfitting in Synthesis: Theory and Practice (Extended Version)
Saswat Padhi, Todd Millstein, Aditya Nori +1
In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatically generate a program belonging to a grammar of possible implementations that meets a logical specificatio…
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…
Adaptive Neural Trees
Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander +2
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the la…
Autofocus Layer for Semantic Segmentation
Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha +4
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus lay…
Quantifying Program Bias
Aws Albarghouthi, Loris D'Antoni, Samuel Drews +1
With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that we aggressively investigate whether programs are biased. We propose a…
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig +8
Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performan…
Fairness as a Program Property
Aws Albarghouthi, Loris D'Antoni, Samuel Drews +1
We explore the following question: Is a decision-making program fair, for some useful definition of fairness? First, we describe how several algorithmic fairness questions can be p…
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…