3 accepted papers
ML models take on a new life after deployment and raise a host of new challenges: data drift, model recalibration and monitoring. If performance erodes over time, engineers in charge may ask what changed – did the data distribution change, did the model get worse after retraining? We propose a flexi…
In recent years exemplar clustering has become a popular tool for applications in document and video summarization, active learning, and clustering with general similarity, where cluster centroids are required to be a subset of the data samples rather than their linear combinations. The problem is…
Logarithms of determinants of large positive definite matrices appear ubiquitously in machine learning applications including Gaussian graphical and Gaussian process models, partition functions of discrete graphical models, minimum-volume ellipsoids and metric and kernel learning. Log-determinant co…