February 7, 2018
Conference Paper

Interactive Machine Learning at Scale with CHISSL


We demonstrate CHISSL, a scalable client-server system for real-time interactive machine learning. Our system is capa- ble of incorporating user feedback incrementally and imme- diately without a structured or pre-defined prediction task. Computation is partitioned between a lightweight web-client and a heavyweight server. The server relies on representation learning and agglomerative clustering to learn a dendrogram, a hierarchical approximation of a representation space. The client uses only this dendrogram to incorporate user feedback into the model via transduction. Distances and predictions for each unlabeled instance are updated incrementally and deter- ministically, with O(n) space and time complexity. Our al- gorithm is implemented in a functional prototype, designed to be easy to use by non-experts. The prototype organizes the large amounts of data into recommendations. This allows the user to interact with actual instances by dragging and drop- ping to provide feedback in an intuitive manner. We applied CHISSL to several domains including cyber, social media, and geo-temporal analysis.

Revised: May 31, 2018 | Published: February 7, 2018


Arendt D.L., E.A. Grace, and S. Volkova. 2018. Interactive Machine Learning at Scale with CHISSL. In The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018), February 2-7, 2018, New Orleans, Louisiana, 8194-8195. Palo Alto, California:Association for the Advancement of Artificial Intelligence. PNNL-SA-129748.