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08/20/2023
Improved Differentially Private Densest Subgraph: Local and Purely Additive
We study the Densest Subgraph problem under the additional constraint of...
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08/09/2023
Controlling Tail Risk in Online Ski-Rental
The classical ski-rental problem admits a textbook 2-competitive determi...
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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
Predictive Flows for Faster Ford-Fulkerson
Recent work has shown that leveraging learned predictions can improve th...
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03/01/2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
ML models are ubiquitous in real world applications and are a constant f...
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01/13/2023
Differentially Private Continual Releases of Streaming Frequency Moment Estimations
The streaming model of computation is a popular approach for working wit...
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10/22/2022
Algorithms with Prediction Portfolios
The research area of algorithms with predictions has seen recent success...
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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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08/15/2022
Easy Differentially Private Linear Regression
Linear regression is a fundamental tool for statistical analysis. This h...
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07/13/2022
Smooth Anonymity for Sparse Binary Matrices
When working with user data providing well-defined privacy guarantees is...
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06/17/2022
Scalable Differentially Private Clustering via Hierarchically Separated Trees
We study the private k-median and k-means clustering problem in d dimens...
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02/18/2022
Learning Predictions for Algorithms with Predictions
A burgeoning paradigm in algorithm design is the field of algorithms wit...
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01/27/2022
Plume: Differential Privacy at Scale
Differential privacy has become the standard for private data analysis, ...
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10/05/2021
Label differential privacy via clustering
We present new mechanisms for label differential privacy, a relaxation o...
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07/20/2021
Faster Matchings via Learned Duals
A recent line of research investigates how algorithms can be augmented w...
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11/13/2020
Secretaries with Advice
The secretary problem is probably the purest model of decision making un...
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07/02/2020
Private Optimization Without Constraint Violations
We study the problem of differentially private optimization with linear ...
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06/18/2020
Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to...
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06/16/2020
Algorithms with Predictions
We introduce algorithms that use predictions from machine learning appli...
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06/10/2020
Sliding Window Algorithms for k-Clustering Problems
The sliding window model of computation captures scenarios in which data...
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02/15/2018
Fair Clustering Through Fairlets
We study the question of fair clustering under the disparate impact doc...
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02/15/2018
Competitive caching with machine learned advice
Traditional online algorithms encapsulate decision making under uncertai...
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02/14/2018
