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One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes
Aravind Reddy · Ryan A. Rossi · Zhao Song · Anup Rao · Tung Mai · Nedim Lipka · Gang Wu · Eunyee Koh · Nesreen K Ahmed

Tue Jul 19 11:50 AM -- 11:55 AM (PDT) @ Hall G
in Theory »

In this paper, we initiate the study of one-pass algorithms for solving the maximum-a-posteriori (MAP) inference problem for Non-symmetric Determinantal Point Processes (NDPPs). In particular, we formulate streaming and online versions of the problem and provide one-pass algorithms for solving these problems. In our streaming setting, data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory, and only need to output a valid solution at the end of the stream. Our online setting has an additional requirement of maintaining a valid solution at any point in time. We design new one-pass algorithms for these problems and show that they perform comparably to (or even better than) the offline greedy algorithm while using substantially lower memory.

Keywords: OPT: Discrete and Combinatorial Optimization · T: Probabilistic Methods · MISC: Scalable Algorithms

Author Information

Aravind Reddy (Northwestern University)
Ryan A. Rossi (Adobe Research)
Zhao Song (Adobe Research)
Anup Rao (Adobe Research)
Tung Mai (Adobe Research)
Nedim Lipka (Adobe Research)
Gang Wu (Adobe Research)
Eunyee Koh (Adobe)
Nesreen K Ahmed (Intel AI Research)

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