60 citations · 229 across the 40 of their papers we have counts for
6 papers · 1 filter
Exploring Connections Between Active Learning and Model Extraction
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli +2
Machine learning is being increasingly used by individuals, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud…
Differentially Private Continual Release of Graph Statistics
Shuang Song, Susan Little, Sanjay Mehta +2
Motivated by understanding the dynamics of sensitive social networks over time, we consider the problem of continual release of statistics in a network that arrives online, while p…
The Inductive Bias of Restricted f-GANs
Shuang Liu, Kamalika Chaudhuri
Generative adversarial networks are a novel method for statistical inference that have achieved much empirical success; however, the factors contributing to this success remain ill…
Data Poisoning Attacks against Online Learning
Yizhen Wang, Kamalika Chaudhuri
We consider data poisoning attacks, a class of adversarial attacks on machine learning where an adversary has the power to alter a small fraction of the training data in order to m…
Spectral Learning of Binomial HMMs for DNA Methylation Data
Chicheng Zhang, Eran A. Mukamel, Kamalika Chaudhuri
We consider learning parameters of Binomial Hidden Markov Models, which may be used to model DNA methylation data. The standard algorithm for the problem is EM, which is computatio…
Active Learning with Logged Data
Songbai Yan, Kamalika Chaudhuri, Tara Javidi
We consider active learning with logged data, where labeled examples are drawn conditioned on a predetermined logging policy, and the goal is to learn a classifier on the entire po…