18 citations · 43 across the 16 of their papers we have counts for
17 papers
Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits
Vladimir Braverman, Chen Wang, Liudeng Wang +1
Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this paper. In this setting, we are…
Online Learning with Limited Information in the Sliding Window Model
Vladimir Braverman, Sumegha Garg, Chen Wang +2
Motivated by recent work on the experts problem in the streaming model, we consider the experts problem in the sliding window model. The sliding window model is a well-studied mode…
Learning-Augmented Hierarchical Clustering
Vladimir Braverman, Jon C. Ergun, Chen Wang +1
Hierarchical clustering (HC) is an important data analysis technique in which the goal is to recursively partition a dataset into a tree-like structure while grouping together simi…
Relative Error Fair Clustering in the Weak-Strong Oracle Model
Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H. -C. Jiang +4
We study fair clustering problems in a setting where distance information is obtained from two sources: a strong oracle providing exact distances, but at a high cost, and a weak or…
On the Price of Differential Privacy for Hierarchical Clustering
Chengyuan Deng, Jie Gao, Jalaj Upadhyay +2
Hierarchical clustering is a fundamental unsupervised machine learning task with the aim of organizing data into a hierarchy of clusters. Many applications of hierarchical clusteri…
Private Data Stream Analysis for Universal Symmetric Norm Estimation
Vladimir Braverman, Joel Manning, Zhiwei Steven Wu +1
We study how to release summary statistics on a data stream subject to the constraint of differential privacy. In particular, we focus on releasing the family of symmetric norms, w…