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05/25/2023
Learning across Data Owners with Joint Differential Privacy
In this paper, we study the setting in which data owners train machine l...
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01/31/2023
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Hierarchical Clustering is a popular unsupervised machine learning metho...
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07/19/2022
Regret Minimization with Noisy Observations
In a typical optimization problem, the task is to pick one of a number o...
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10/29/2021
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches
Streaming computation plays an important role in large-scale data analys...
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06/04/2021
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering
In this paper, we study the r-gather problem, a natural formulation of m...
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02/23/2021
Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT
In this paper, we study a number of well-known combinatorial optimizatio...
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01/26/2021
Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering
Recently, Hierarchical Clustering (HC) has been considered through the l...
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10/22/2020
Optimal Approximation – Smoothness Tradeoffs for Soft-Max Functions
A soft-max function has two main efficiency measures: (1) approximation ...
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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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02/06/2020
Fair Correlation Clustering
In this paper, we study correlation clustering under fairness constraint...
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12/15/2019
Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection
Hierarchical Clustering is an unsupervised data analysis method which ha...
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11/05/2019
Response Prediction for Low-Regret Agents
Companies like Google and Microsoft run billions of auctions every day t...
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05/29/2019
Clustering without Over-Representation
In this paper we consider clustering problems in which each point is end...
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09/25/2018