Abstract 
In this paper we consider batch classification: we have an input space X and an unknown labelling of points in X. We assume there is a probability distribution over X and N points are sampled i.i.d. from the distribution. The N points are given to the learner, along with their labels, and it is the goal of the learner to build a function that labels new points in X correctly (most of the time). We will consider algorithms for the learner that work via onlinetobatch conversion described as follows. Online classification is the following game. We have a number of trials. On each trial: 1) A point in X is revealed to the learner. 2) The learner predicts the label of the point. 3) The true label is revealed to the learner. Online to batch classification is the process of converting an online classification algorithm into a batch classification algorithm. We consider the case when the given point/label pairs are distributed across different machines. We give a distributed implementation of the onlinetobatch conversion process that requires only a small amount of data to be broadcast. 
Authors 
 Stephen Pasteris (UCL)
 Mark Herbster (UCL)
 Richard Tomsett (IBM UK)
 Shiqiang Wang (IBM US)

Date 
Sep2020 
Venue 
4th Annual Fall Meeting of the DAIS ITA, 2020 
