281 citations · 461 across the 8 of their papers we have counts for
10 papers
Nonasymptotic Heavy-Tailed Mean Estimation in Smooth Banach Spaces
Justin Whitehouse, Ben Chugg, Diego Martinez-Taboada +1
We consider the problem of nonasymptotic mean estimation for heavy-tailed random variables taking values in a smooth Banach space. In particular, we extend and improve a simple tru…
Orthogonal Causal Calibration
Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis +2
Estimates of heterogeneous treatment effects such as conditional average treatment effects (CATEs) and conditional quantile treatment effects (CQTEs) play an important role in real…
Time-Uniform Self-Normalized Concentration for Vector-Valued Processes
Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
Self-normalized processes arise naturally in many learning-related tasks. While self-normalized concentration has been extensively studied for scalar-valued processes, there are fe…
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…
Optimal Resource Allocation for Elastic and Inelastic Jobs
Benjamin Berg, Mor Harchol-Balter, Benjamin Moseley +2
Modern data centers are tasked with processing heterogeneous workloads consisting of various classes of jobs. These classes differ in their arrival rates, size distributions, and j…
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…
Formal Security Analysis of Neural Networks using Symbolic Intervals
Shiqi Wang, Kexin Pei, Justin Whitehouse +2
Due to the increasing deployment of Deep Neural Networks (DNNs) in real-world security-critical domains including autonomous vehicles and collision avoidance systems, formally chec…