83 citations · 133 across the 18 of their papers we have counts for
20 papers
Learning across Data Owners with Joint Differential Privacy
Yangsibo Huang, Haotian Jiang, Daogao Liu +3
In this paper, we study the setting in which data owners train machine learning models collaboratively under a privacy notion called joint differential privacy [Kearns et al., 2018…
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Jacob Imola, Alessandro Epasto, Mohammad Mahdian +2
Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approxi…
Regret Minimization with Noisy Observations
Mohammad Mahdian, Jieming Mao, Kangning Wang
In a typical optimization problem, the task is to pick one of a number of options with the lowest cost or the highest value. In practice, these cost/value quantities often come thr…
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni +1
Streaming computation plays an important role in large-scale data analysis. The sliding window model is a model of streaming computation which also captures the recency of the data…
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni +1
In this paper, we study the -gather problem, a natural formulation of minimum-size clustering in metric spaces. The goal of -gather is to partition points into clusters s…
Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT
Vaggos Chatziafratis, Mohammad Mahdian, Sara Ahmadian
In this paper, we study a number of well-known combinatorial optimization problems that fit in the following paradigm: the input is a collection of (potentially inconsistent) local…