36 citations · 57 across the 6 of their papers we have counts for
10 papers
Learning to be adversarially robust and differentially private
Jamie Hayes, Borja Balle, M. Pawan Kumar
We study the difficulties in learning that arise from robust and differentially private optimization. We first study convergence of gradient descent based adversarial training with…
Towards transformation-resilient provenance detection of digital media
Jamie Hayes, Krishnamurthy, Dvijotham +4
Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are difficult to distinguish from natural signals, creating opportu…
Extensions and limitations of randomized smoothing for robustness guarantees
Jamie Hayes
Randomized smoothing, a method to certify a classifier's decision on an input is invariant under adversarial noise, offers attractive advantages over other certification methods. I…
Unique properties of adversarially trained linear classifiers on Gaussian data
Jamie Hayes
Machine learning models are vulnerable to adversarial perturbations, that when added to an input, can cause high confidence misclassifications. The adversarial learning research co…
Contamination Attacks and Mitigation in Multi-Party Machine Learning
Jamie Hayes, Olga Ohrimenko
Machine learning is data hungry; the more data a model has access to in training, the more likely it is to perform well at inference time. Distinct parties may want to combine thei…
A note on hyperparameters in black-box adversarial examples
Jamie Hayes
Since Biggio et al. (2013) and Szegedy et al. (2013) first drew attention to adversarial examples, there has been a flood of research into defending and attacking machine learning…