9 citations · 10 across the 6 of their papers we have counts for
6 papers
Fast Private Adaptive Query Answering for Large Data Domains
Miguel Fuentes, Brett Mullins, Yingtai Xiao +3
Privately releasing marginals of a tabular dataset is a foundational problem in differential privacy. However, state-of-the-art mechanisms suffer from a computational bottleneck wh…
Efficient and Private Marginal Reconstruction with Local Non-Negativity
Brett Mullins, Miguel Fuentes, Yingtai Xiao +3
Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private…
Kernel Interpolation with Sparse Grids
Mohit Yadav, Daniel Sheldon, Cameron Musco
Structured kernel interpolation (SKI) accelerates Gaussian process (GP) inference by interpolating the kernel covariance function using a dense grid of inducing points, whose corre…
Faster Kernel Interpolation for Gaussian Processes
Mohit Yadav, Daniel Sheldon, Cameron Musco
A key challenge in scaling Gaussian Process (GP) regression to massive datasets is that exact inference requires computation with a dense n x n kernel matrix, where n is the number…
Toward a Characterization of Loss Functions for Distribution Learning
Nika Haghtalab, Cameron Musco, Bo Waggoner
In this work we study loss functions for learning and evaluating probability distributions over large discrete domains. Unlike classification or regression where a wide variety of…
Learning to Prune: Speeding up Repeated Computations
Daniel Alabi, Adam Tauman Kalai, Katrina Ligett +3
It is common to encounter situations where one must solve a sequence of similar computational problems. Running a standard algorithm with worst-case runtime guarantees on each inst…