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06/01/2023
Better Private Linear Regression Through Better Private Feature Selection
Existing work on differentially private linear regression typically assu...
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04/12/2023
Measuring Re-identification Risk
Compact user representations (such as embeddings) form the backbone of p...
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03/01/2023
Subset-Based Instance Optimality in Private Estimation
We propose a new definition of instance optimality for differentially pr...
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02/06/2023
Easy Learning from Label Proportions
We consider the problem of Learning from Label Proportions (LLP), a weak...
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11/06/2022
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics
A reconstruction attack on a private dataset D takes as input some publi...
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10/20/2022
Private Algorithms with Private Predictions
When applying differential privacy to sensitive data, a common way of ge...
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12/19/2020
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning
This paper introduces the first provably accurate algorithms for differe...
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06/12/2020
Algorithms and Learning for Fair Portfolio Design
We consider a variation on the classical finance problem of optimal port...
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02/10/2020
Random Smoothing Might be Unable to Certify ℓ_∞ Robustness for High-Dimensional Images
We show a hardness result for random smoothing to achieve certified adve...
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08/08/2019
How much data is sufficient to learn high-performing algorithms?
Algorithms for scientific analysis typically have tunable parameters tha...
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07/22/2019
Online optimization of piecewise Lipschitz functions in changing environments
In an online optimization problem we are required to choose a sequence o...
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07/01/2019
Learning to Link
Clustering is an important part of many modern data analysis pipelines, ...
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04/18/2019
Semi-bandit Optimization in the Dispersed Setting
In this work, we study the problem of online optimization of piecewise L...
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09/23/2018
Envy-Free Classification
In classic fair division problems such as cake cutting and rent division...
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09/19/2018
Data-Driven Clustering via Parameterized Lloyd's Families
Algorithms for clustering points in metric spaces is a long-studied area...
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03/27/2018
Learning to Branch
Tree search algorithms, such as branch-and-bound, are the most widely us...
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12/15/2015
