81 citations · 139 across the 15 of their papers we have counts for
16 papers
DP-Auditorium: a Large Scale Library for Auditing Differential Privacy
William Kong, Andrés Muñoz Medina, Mónica Ribero +1
New regulations and increased awareness of data privacy have led to the deployment of new and more efficient differentially private mechanisms across public institutions and indust…
Private and Communication-Efficient Algorithms for Entropy Estimation
Gecia Bravo-Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh +2
Modern statistical estimation is often performed in a distributed setting where each sample belongs to a single user who shares their data with a central server. Users are typicall…
Measuring Re-identification Risk
CJ Carey, Travis Dick, Alessandro Epasto +8
Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification r…
Easy Learning from Label Proportions
Robert Istvan Busa-Fekete, Heejin Choi, Travis Dick +2
We consider the problem of Learning from Label Proportions (LLP), a weakly supervised classification setup where instances are grouped into "bags", and only the frequency of class…
Differentially Private Continual Releases of Streaming Frequency Moment Estimations
Alessandro Epasto, Jieming Mao, Andres Munoz Medina +3
The streaming model of computation is a popular approach for working with large-scale data. In this setting, there is a stream of items and the goal is to compute the desired quant…
Smooth Anonymity for Sparse Graphs
Alessandro Epasto, Hossein Esfandiari, Vahab Mirrokni +1
When working with user data providing well-defined privacy guarantees is paramount. In this work, we aim to manipulate and share an entire sparse dataset with a third party private…
Statistical anonymity: Quantifying reidentification risks without reidentifying users
Gecia Bravo-Hermsdorff, Robert Busa-Fekete, Lee M. Gunderson +2
Data anonymization is an approach to privacy-preserving data release aimed at preventing participants reidentification, and it is an important alternative to differential privacy i…
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…
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…
Private Optimization Without Constraint Violations
Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii +1
We study the problem of differentially private optimization with linear constraints when the right-hand-side of the constraints depends on private data. This type of problem appear…
Online Learning for Non-Stationary A/B Tests
Andrés Muñoz Medina, Sergei Vassilvitskii, Dong Yin
The rollout of new versions of a feature in modern applications is a manual multi-stage process, as the feature is released to ever larger groups of users, while its performance is…
Revenue Optimization with Approximate Bid Predictions
Andrés Muñoz Medina, Sergei Vassilvitskii
In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types and the…
Non-parametric Revenue Optimization for Generalized Second Price Auctions
Mehryar Mohri, Andres Munoz Medina
We present an extensive analysis of the key problem of learning optimal reserve prices for generalized second price auctions. We describe two algorithms for this task: one based on…
Revenue Optimization in Posted-Price Auctions with Strategic Buyers
Mehryar Mohri, Andres Muñoz Medina
We study revenue optimization learning algorithms for posted-price auctions with strategic buyers. We analyze a very broad family of monotone regret minimization algorithms for thi…
Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve
Mehryar Mohri, Andres Muñoz Medina
Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly de- pends on the outco…
New Analysis and Algorithm for Learning with Drifting Distributions
Mehryar Mohri, Andres Munoz Medina
We present a new analysis of the problem of learning with drifting distributions in the batch setting using the notion of discrepancy. We prove learning bounds based on the Rademac…