39 citations · 308 across the 129 of their papers we have counts for
17 papers · 1 filter
Private Rank Aggregation in Central and Local Models
Daniel Alabi, Badih Ghazi, Ravi Kumar +1
In social choice theory, (Kemeny) rank aggregation is a well-studied problem where the goal is to combine rankings from multiple voters into a single ranking on the same set of ite…
The Price of Justified Representation
Edith Elkind, Piotr Faliszewski, Ayumi Igarashi +3
In multiwinner approval voting, the goal is to select -member committees based on voters' approval ballots. A well-studied concept of proportionality in this context is the just…
Private Robust Estimation by Stabilizing Convex Relaxations
Pravesh K. Kothari, Pasin Manurangsi, Ameya Velingker
We give the first polynomial time and sample -differentially private (DP) algorithm to estimate the mean, covariance and higher moments in the presence of a constant fracti…
Tight Bounds for Differentially Private Anonymized Histograms
Pasin Manurangsi
In this note, we consider the problem of differentially privately (DP) computing an anonymized histogram, which is defined as the multiset of counts of the input dataset (without b…
User-Level Private Learning via Correlated Sampling
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample. In this work, we consider the setting where each user holds $…
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
Badih Ghazi, Ravi Kumar, Pasin Manurangsi +2
The shuffle model of differential privacy has attracted attention in the literature due to it being a middle ground between the well-studied central and local models. In this work,…
Large-Scale Differentially Private BERT
Rohan Anil, Badih Ghazi, Vineet Gupta +2
In this work, we study the large-scale pretraining of BERT-Large with differentially private SGD (DP-SGD). We show that combined with a careful implementation, scaling up the batch…
Justifying Groups in Multiwinner Approval Voting
Edith Elkind, Piotr Faliszewski, Ayumi Igarashi +3
Justified representation (JR) is a standard notion of representation in multiwinner approval voting. Not only does a JR committee always exist, but previous work has also shown thr…
Google COVID-19 Vaccination Search Insights: Anonymization Process Description
Shailesh Bavadekar, Adam Boulanger, John Davis +22
This report describes the aggregation and anonymization process applied to the COVID-19 Vaccination Search Insights (published at http://goo.gle/covid19vaccinationinsights), a publ…
Linear Discrepancy is -Hard to Approximate
Pasin Manurangsi
In this note, we prove that the problem of computing the linear discrepancy of a given matrix is -hard, even to approximate within factor for any . This streng…
On the Complexity of Fair House Allocation
Naoyuki Kamiyama, Pasin Manurangsi, Warut Suksompong
We study fairness in house allocation, where houses are to be allocated among agents so that every agent receives one house. We show that maximizing the number of envy-free…
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias +3
We consider the following variant of contextual linear bandits motivated by routing applications in navigational engines and recommendation systems. We wish to learn a hidden -d…
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Badih Ghazi, Ravi Kumar, Pasin Manurangsi +1
Differential privacy (DP) is a formal notion for quantifying the privacy loss of algorithms. Algorithms in the central model of DP achieve high accuracy but make the strongest trus…
Locally Private k-Means in One Round
Alisa Chang, Badih Ghazi, Ravi Kumar +1
We provide an approximation algorithm for k-means clustering in the one-round (aka non-interactive) local model of differential privacy (DP). This algorithm achieves an approximati…
Almost Envy-Freeness for Groups: Improved Bounds via Discrepancy Theory
Pasin Manurangsi, Warut Suksompong
We study the allocation of indivisible goods among groups of agents using well-known fairness notions such as envy-freeness and proportionality. While these notions cannot always b…
Generalized Kings and Single-Elimination Winners in Random Tournaments
Pasin Manurangsi, Warut Suksompong
Tournaments can be used to model a variety of practical scenarios including sports competitions and elections. A natural notion of strength of alternatives in a tournament is a gen…
Deep Learning with Label Differential Privacy
Badih Ghazi, Noah Golowich, Ravi Kumar +2
The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy g…