Societal and real-world considerations such as robustness, fairness, social welfare and multi-
agent tradeoffs have given rise to multi-distribution learning paradigms, such as …

This paper provides new algorithms for distributed clustering for two popular center-based
objectives, $ k $-median and $ k $-means. These algorithms have provable guarantees and …

We consider the problem of PAC-learning from distributed data and analyze fundamental
communication complexity questions involved. We provide general upper and lower bounds …

We consider parameter estimation in distributed networks, where each sensor in the network
observes an independent sample from an underlying distribution and has $ k $ bits to …

We study exploration in Multi-Armed Bandits (MAB) in a setting where~ $ k $ players
collaborate in order to identify an $\epsilon $-optimal arm. Our motivation comes from recent …

We explore the connection between dimensionality and communication cost in distributed
learning problems. Specifically we study the problem of estimating the mean $\vectheta $ of …

We consider a number of fundamental statistical and graph problems in the message-
passing model, where we have kk machines (sites), each holding a piece of data, and the …

We initiate a systematic investigation of distribution learning (density estimation) when the
data is distributed across multiple servers. The servers must communicate with a referee and …

We consider the following problem in this paper: given a set of n distributions, find the top-m
ones with the largest means. This problem is also called top-m arm identifications in the …

We present the first protocol for distributed online prediction that aims to minimize online
prediction loss and network communication at the same time. This protocol can be applied …