CIF: Medium: Learning, refining, and understanding models through relational feedback

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Most machine intelligence systems require cooperation between a learning algorithm and an oracle expert providing supervision. There are many rich learning scenarios in which data from the oracle consists of relational feedback, either in terms of partial orders or preferences among items, labels indicating the perception of similarity or differences between items, or rich combinations thereof when the oracle executes a sequence of decisions to optimize her own utility. In all of these settings, there is a data space that is implicit to the oracle, and the investigators wish to exploit structure in this space (i.e., learn), to judiciously elicit and combine knowledge from a variety of data sources to accelerate this process (i.e., refine), and to transfer aspects of this structure into other learned models (i.e., understand). The investigators propose a collaborative research agenda that will transform the ability to learn, refine, and understand models through relational feedback in two distinct ways: (a) by exploiting latent space representations to build better models and algorithms, and (b) by exploiting new relational query paradigms for more efficient, effective, and interpretable information extraction from oracles. The investigators are involving students at all levels and across multiple departments in this highly interdisciplinary research effort that is expected to have broad applications, including (but not limited to) information-retrieval systems, (inverse) reinforcement learning, and psychophysical experimental design. The results of the proposed research are intended to improve the understanding of ranking systems impacting how content is displayed on news and social media sites, and even how hiring and college admissions is conducted. The researchers also intend to develop new tools for aggregating subjective human preferences that use relational queries to align AI systems with human values. Additionally, the PIs are devoting resources from this project to broaden participation among traditionally under-represented groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.