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Sergei Vassilvtiskii

7 accepted papers

2021

Hierarchical Clustering in General Metric Spaces using Approximate Nearest Neighbors

AISTATS 2021poster

Hierarchical clustering is a widely used data analysis method, but suffers from scalability issues, requiring quadratic time in general metric spaces. In this work, we demonstrate how approximate nearest neighbor (ANN) queries can be used to improve the running time of the popular single-linkage and…

Cited by 15SourcePDFScholar
2021

Private optimization without constraint violations

AISTATS 2021poster

We study the problem of differentially private optimization with linear constraints when the right-hand-side of the constraints depends on private data. This type of problem appears in many applications, especially resource allocation. Previous research provided solutions that retained privacy but s…

Cited by 10SourcePDFScholar
2019

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes

ICML 2019oral

It is often desirable in recommender systems and other information retrieval applications to provide diverse results, and determinantal point processes (DPPs) have become a popular way to capture the trade-off between the quality of individual results and the diversity of the overall set. However, s…

Cited by 31SourcePDFScholar
2019

Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy

ICML 2019oral

Differentially private learning algorithms protect individual participants in the training dataset by guaranteeing that their presence does not significantly change the resulting model. In order to make this promise, such algorithms need to know the maximum contribution that can be made by a single…

Cited by 93SourcePDFScholar
2019

Consistent Online Optimization: Convex and Submodular

AISTATS 2019poster

Modern online learning algorithms achieve low (sublinear) regret in a variety of diverse settings. These algorithms, however, update their solution at every time step. While these updates are computationally efficient, the very requirement of frequent updates makes the algorithms untenable in some p…

Cited by 18SourcePDFScholar