Research
In short, the goal of my research is to create efficient methods that learn a model of similarity between objects from human feedback. Humans can provide unique insight into how objects relate that raw features cannot capture. Human intuition has been used to guide many object recognition, document retrieval, and medical diagnosis techniques, just to name a few. My research aims to effectively capture how humans think about the relationships among objects in a model that intelligent systems can use to perform various tasks. This has multiple inherent challenges:
- How do I prompt humans for feedback effectively?
- How do I make the most of the feedback I am given?
- What models are desirable for use in intelligent systems?
- How do I learn these models efficiently?
The bulk of my previous work has focused on learning a nonparametric positive semidefinite kernel matrix from relative comparison feedback. Many machine learning methods are kernelized, thus a kernel matrix can be easily utilized by such kernelized methods. Relative comparisons (which we define as comparisons of the form "Object A is more similar to object B than object C") are intuitive for humans to provide, and can be extracted from many human/computer interactions, thus making them an effective means for obtaining similarity from humans. I have works detailing methods that aim to learn accurate models with little human feedback in an efficient manner.
My current work has two main goals. First, I want to create methods that learn accurate models of similarity among a large number of objects for application to web-scale data. Learning similarity amongst an enormous number of objects poses an obvious challenge due to scale, but also due to the potential variation in such a large collection of objects. Second, I wish to create efficient active learning techniques that can be used in conjunction with methods that learn a model of similarity. By doing so, a similarity model-learning method can make the most out of human feedback, which is often time-consuming and costly to obtain.
