Man Machine Machine Learning With Humans-in-the-Loop
Intelligent systems, ranging from internet search engines and online retailers to personal robots and MOOCs, live in a symbiotic relationship with their users - or at least they should. On the one hand, users greatly benefit from the services provided by these systems. On the other hand, these systems can greatly benefit from the world knowledge that users communicate through their interactions with the system. These interactions -- queries, clicks, votes, purchases, answers, demonstrations, etc. -- provide enormous potential for economically and autonomously optimizing these systems and for gaining unprecedented amounts of world knowledge required to solve some of the hardest AI problems.
In this talk I discuss the challenges of learning from data that results from human behavior. I will present new machine learning models and algorithms that explicitly account for the human decision making process and factors underlying it such as human expertise, skills and needs. The talk will also explore how we can look to optimize human interactions to build robust learning systems with provable performance guarantees. I will also present examples, from the domains of search, recommendation and educational analytics, where we have successfully deployed systems for cost-effectively learning with humans in the loop.
Karthik Raman is a PhD student at Cornell University working with Prof. Thorsten Joachims. Motivated by applications such as search, recommendation and educational analytics, his research aims to tackle learning problems with a human-in-the-loop. His work on understanding the role of diversity in complex search tasks won a best paper award at SIGIR. He is supported by a Google PhD Fellowship and a Yahoo Key Scientific Challenge Award.