5 citations · 21 across the 17 of their papers we have counts for
9 papers · 1 filter
Asymptotic Convergence of Thompson Sampling
Cem Kalkanli, Ayfer Ozgur
Thompson sampling has been shown to be an effective policy across a variety of online learning tasks. Many works have analyzed the finite time performance of Thompson sampling, and…
Strong Privacy and Utility Guarantee: Over-the-Air Statistical Estimation
Wenhao Zhan
We consider the privacy problem of statistical estimation from distributed data, where users communicate to a central processor over a Gaussian multiple-access channel(MAC). To avo…
Information Constrained Optimal Transport: From Talagrand, to Marton, to Cover
Yikun Bai, Xiugang Wu, Ayfer Ozgur
The optimal transport problem studies how to transport one measure to another in the most cost-effective way and has wide range of applications from economics to machine learning.…
Breaking the Communication-Privacy-Accuracy Trilemma
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
Two major challenges in distributed learning and estimation are 1) preserving the privacy of the local samples; and 2) communicating them efficiently to a central server, while ach…
Lower Bounds and a Near-Optimal Shrinkage Estimator for Least Squares using Random Projections
Srivatsan Sridhar, Mert Pilanci, Ayfer Özgür
In this work, we consider the deterministic optimization using random projections as a statistical estimation problem, where the squared distance between the predictions from the e…
Fisher information under local differential privacy
Leighton Pate Barnes, Wei-Ning Chen, Ayfer Ozgur
We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter under local differential pr…