3 accepted papers
We study the fair variant of the classic k-median problem introduced by (Chierichetti et al., NeurIPS 2017) in which the points are colored, and the goal is to minimize the same average distance objective as in the standard $k$-median problem while ensuring that all clusters have an “approximately e…
In the most popular distribution testing and parameter estimation model, one can obtain information about an underlying distribution D via independent samples from D. We introduce a model in which every sample comes with the information about the probability of selecting it. In this setting, we give…
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 the goal is to estimate the underlying distribution with as few bits of communication as possible. We focus on no…