Machine Learning in Montpellier, Theory & Practice
We will present the recent Active Clustering Problem (ACP). In this problem, a set of items can be partitioned into groups where items within the same group are characterised by the same multi-dimensional vector. A learner obtains noisy observations of these vectors, and we consider an active setting where the learner chooses the order and the number of observations. The objective is to recover the hidden partition of the items, using as few requests as possible.In the presentation, I will explain the ACP and answer two questions. Can we improve upon the number of requests of the simple uniform sampling algorithm, using the benefits of active sampling ? Is there a fundamental computation-information gap for clustering in high-dimension with repeated measurements?