12 citations · 34 across the 5 of their papers we have counts for
7 papers
On the (Un-)Avoidability of Adversarial Examples
Sadia Chowdhury, Ruth Urner
The phenomenon of adversarial examples in deep learning models has caused substantial concern over their reliability. While many deep neural networks have shown impressive performa…
Black-box Certification and Learning under Adversarial Perturbations
Hassan Ashtiani, Vinayak Pathak, Ruth Urner
We formally study the problem of classification under adversarial perturbations from a learner's perspective as well as a third-party who aims at certifying the robustness of a giv…
When can unlabeled data improve the learning rate?
Christina Göpfert, Shai Ben-David, Olivier Bousquet +3
In semi-supervised classification, one is given access both to labeled and unlabeled data. As unlabeled data is typically cheaper to acquire than labeled data, this setup becomes a…
Active Nearest-Neighbor Learning in Metric Spaces
Aryeh Kontorovich, Sivan Sabato, Ruth Urner
We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a near…
Efficient Learning of Linear Separators under Bounded Noise
Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab +1
We study the learnability of linear separators in in the presence of bounded (a.k.a Massart) noise. This is a realistic generalization of the random classification noise mo…
Learning Economic Parameters from Revealed Preferences
Maria-Florina Balcan, Amit Daniely, Ruta Mehta +2
A recent line of work, starting with Beigman and Vohra (2006) and Zadimoghaddam and Roth (2012), has addressed the problem of {\em learning} a utility function from revealed prefer…
Generative Multiple-Instance Learning Models For Quantitative Electromyography
Tameem Adel, Benn Smith, Ruth Urner +2
We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped to…