36 citations · 56 across the 3 of their papers we have counts for
3 papers
cs.LG2014★ 20 cited
The Large Margin Mechanism for Differentially Private Maximization
Kamalika Chaudhuri, Daniel Hsu, Shuang Song
A basic problem in the design of privacy-preserving algorithms is the private maximization problem: the goal is to pick an item from a universe that (approximately) maximizes a dat…
cs.LG2014★ 36 cited
Beyond Disagreement-based Agnostic Active Learning
Chicheng Zhang, Kamalika Chaudhuri
We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no…
cs.LG2012
An Online Learning-based Framework for Tracking
Kamalika Chaudhuri, Yoav Freund, Daniel Hsu
We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem i…