Rupam Acharyya

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We develop a new statistical machine learning paradigm, named infinite-label learning, to annotate a data point with more than one relevant labels from a candidate set, which pools both the finite labels observed at training and a potentially infinite number of previously unseen labels. The infinite-label learning fundamentally expands the scope of(More)
We study the problem of counting the number of popular matchings in a given instance. A popular matching instance consists of agents A and houses H, where each agent ranks a subset of houses according to their preferences. A matching is an assignment of agents to houses. A matching M is more popular than matching M ′ if the number of agents that prefer M to(More)
Project on Counting Problems Rupam Acharyya Recognizing that many of the most creative endeavors in history have been accomplished by very young people, the Federal Government has decided to offer ITRG graduate awards. These awards are intended to support highly innovative research in information technology primarily conceived by students under the age of(More)
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