542 citations · 1.6k across the 85 of their papers we have counts for
9 papers · 1 filter
Variational Bayes In Private Settings (VIPS)
Mijung Park, James Foulds, Kamalika Chaudhuri +1
Many applications of Bayesian data analysis involve sensitive information, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving fram…
Active Learning from Imperfect Labelers
Songbai Yan, Kamalika Chaudhuri, Tara Javidi
We study active learning where the labeler can not only return incorrect labels but also abstain from labeling. We consider different noise and abstention conditions of the labeler…
Private Topic Modeling
Mijung Park, James Foulds, Kamalika Chaudhuri +1
We develop a privatised stochastic variational inference method for Latent Dirichlet Allocation (LDA). The iterative nature of stochastic variational inference presents challenges:…
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Xi Wu, Fengan Li, Arun Kumar +3
While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics framew…
DP-EM: Differentially Private Expectation Maximization
Mijung Park, Jimmy Foulds, Kamalika Chaudhuri +1
The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed.…
The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions
Chicheng Zhang, Kamalika Chaudhuri
This paper studies classification with an abstention option in the online setting. In this setting, examples arrive sequentially, the learner is given a hypothesis class $\mathcal…
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis
James Foulds, Joseph Geumlek, Max Welling +1
Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notio…
Pufferfish Privacy Mechanisms for Correlated Data
Shuang Song, Yizhen Wang, Kamalika Chaudhuri
Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical acti…
Convex Optimization For Non-Convex Problems via Column Generation
Julian Yarkony, Kamalika Chaudhuri
We apply column generation to approximating complex structured objects via a set of primitive structured objects under either the cross entropy or L2 loss. We use L1 regularization…