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• W3178783483 abstract "Real-world decision-making systems are often subject to uncertainties that have to be resolved through observational data. Therefore, we are frequently confronted with combinatorial optimization problems of which the objective function is unknown and thus has to be debunked using empirical evidence. In contrast to the common practice that relies on a learning-and-optimization strategy, we consider the regression between combinatorial spaces, aiming to infer high-quality optimization solutions from samples of input-solution pairs -- without the need to learn the objective function. Our main deliverable is a universal solver that is able to handle abstract undetermined stochastic combinatorial optimization problems. For learning foundations, we present learning-error analysis under the PAC-Bayesian framework using a new margin-based analysis. In empirical studies, we demonstrate our design using proof-of-concept experiments, and compare it with other methods that are potentially applicable. Overall, we obtain highly encouraging experimental results for several classic combinatorial problems on both synthetic and real-world datasets." @default.
• W3178783483 created "2021-07-19" @default.
• W3178783483 creator A5011783006 @default.
• W3178783483 date "2021-07-15" @default.
• W3178783483 modified "2023-09-27" @default.
• W3178783483 title "USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems" @default.
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• W3178783483 hasPublicationYear "2021" @default.
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