38 citations · 90 across the 10 of their papers we have counts for
11 papers
Better Private Linear Regression Through Better Private Feature Selection
Travis Dick, Jennifer Gillenwater, Matthew Joseph
Existing work on differentially private linear regression typically assumes that end users can precisely set data bounds or algorithmic hyperparameters. End users often struggle to…
Plume: Differential Privacy at Scale
Kareem Amin, Jennifer Gillenwater, Matthew Joseph +2
Differential privacy has become the standard for private data analysis, and an extensive literature now offers differentially private solutions to a wide variety of problems. Howev…
A Joint Exponential Mechanism For Differentially Private Top-
Jennifer Gillenwater, Matthew Joseph, Andrés Muñoz Medina +1
We present a differentially private algorithm for releasing the sequence of elements with the highest counts from a data domain of elements. The algorithm is a "joint" inst…
Scalable Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Jennifer Gillenwater +2
A determinantal point process (DPP) on a collection of items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items.…
Combining Public and Private Data
Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza
Differential privacy is widely adopted to provide provable privacy guarantees in data analysis. We consider the problem of combining public and private data (and, more generally, d…
Differentially Private Quantiles
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Quantiles are often used for summarizing and understanding data. If that data is sensitive, it may be necessary to compute quantiles in a way that is differentially private, provid…
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing +1
Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that p…
Duff: A Dataset-Distance-Based Utility Function Family for the Exponential Mechanism
Andrés Muñoz Medina, Jenny Gillenwater
We propose and analyze a general-purpose dataset-distance-based utility function family, Duff, for differential privacy's exponential mechanism. Given a particular dataset and a st…
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Mike Gartrell, Insu Han, Elvis Dohmatob +2
Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work sho…
Submodular Hamming Metrics
Jennifer Gillenwater, Rishabh Iyer, Bethany Lusch +2
We show that there is a largely unexplored class of functions (positive polymatroids) that can define proper discrete metrics over pairs of binary vectors and that are fairly tract…
Expectation-Maximization for Learning Determinantal Point Processes
Jennifer Gillenwater, Alex Kulesza, Emily Fox +1
A determinantal point process (DPP) is a probabilistic model of set diversity compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we wo…