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)
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)
We study the problem of counting the number of popular matchings in a given instance. McDermid and Irving gave a poly-time algorithm for counting the number of popular matchings when the preference lists are strictly ordered. We first consider the case of ties in preference lists. Nasre proved that the problem of counting the number of popular matching is(More)
We study the Glauber dynamics for Ising model on (sequences of) dense graphs. We view the dense graphs through the lens of graphons [19]. For the ferromagnetic Ising model with inverse temperature β on a convergent sequence of graphs {Gn} with limit graphon W we show fast mixing of the Glauber dynamics if βλ1(W ) < 1 and slow (torpid) mixing if βλ1(W ) > 1(More)
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