RI: Small: Bayesian Thinking on Your Feet---Embedding Generative Models in Reinforcement Learning for Sequentially Revealed Data

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Machine learning algorithms cannot "think on their feet". When applied in practice, most approaches developed using traditional machine learning techniques wait for an entire input to arrive before they are able to provide an answer or react. While sufficient for some tasks, this is inappropriate for a large class of problems that require more immediate or incremental responses. This project develops new algorithms to address machine learning problems that require an algorithm to "think on its feet". These algorithms combine guesses about what input is likely appear in the future with actions that the algorithm should take now to provide useful, effective output in a timely fashion.One application of these new methods is simultaneous translation. This is the problem of taking problem of "observing" a sentence one word at a time in a foreign language, such as German, and providing a real-time running translation in a target language (like English). This is particularly difficult for language pairs that have significant syntactic divergences, such as object-verb order differences between foreign languages like German or Japanese (verb final) and target languages like English (verb medial). Like human simultaneous translators, machine learning algorithms must learn to predict the words that will appear at the end of a sentence. The project facilitates this prediction using a framework that combined word prediction and machine translation system.The project also uses the newly developed algorithms in academic settings to provide significant outreach to high school students and undergraduates, particularly in underrepresented communities.