281 citations · 461 across the 8 of their papers we have counts for
5 papers · 1 filter
On the Sublinear Regret of GP-UCB
Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
In the kernelized bandit problem, a learner aims to sequentially compute the optimum of a function lying in a reproducing kernel Hilbert space given only noisy evaluations at seque…
Adaptive Principal Component Regression with Applications to Panel Data
Anish Agarwal, Keegan Harris, Justin Whitehouse +1
Principal component regression (PCR) is a popular technique for fixed-design error-in-variables regression, a generalization of the linear regression setting in which the observed…
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints
Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas +1
There is a disconnect between how researchers and practitioners handle privacy-utility tradeoffs. Researchers primarily operate from a privacy first perspective, setting strict pri…
Fully Adaptive Composition in Differential Privacy
Justin Whitehouse, Aaditya Ramdas, Ryan Rogers +1
Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy com…
Efficient Formal Safety Analysis of Neural Networks
Shiqi Wang, Kexin Pei, Justin Whitehouse +2
Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these netw…