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Poster
Optimization from Structured Samples for Coverage Functions
Wei Chen · Xiaoming Sun · Jialin Zhang · Zhijie Zhang

Wed Jul 15 04:00 PM -- 04:45 PM & Thu Jul 16 03:00 AM -- 03:45 AM (PDT) @
in Poster Session 30 »
We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data. Previous results showed that we cannot obtain a constant approximation ratio for the maximum coverage problem using polynomially many independent samples of the form $\{S_i, f(S_i)\}_{i=1}^t$ (Balkanski et al., 2017), even if coverage functions are $(1 - \epsilon)$-PMAC learnable using these samples (Badanidiyuru et al., 2012), which means most of the function values can be approximately learned very well with high probability. In this work, to circumvent the impossibility result of OPS, we propose a stronger model called optimization from structured samples (OPSS) for coverage functions, where the data samples encode the structural information of the functions. We show that under three general assumptions on the sample distributions, we can design efficient OPSS algorithms that achieve a constant approximation for the maximum coverage problem. We further prove a constant lower bound under these assumptions, which is tight when not considering computational efficiency. Moreover, we also show that if we remove any one of the three assumptions, OPSS for the maximum coverage problem has no constant approximation.
Keywords: Combinatorial Optimization · Computational Learning Theory · Large Scale Learning and Big Data · Optimization - General

Author Information

Wei Chen (Microsoft)
Xiaoming Sun (Institute of Computing Technology, Chinese Academy of Sciences )
Jialin Zhang (Institute of Computing Technology, CAS)
Zhijie Zhang (Institute of Computing Technology, Chinese Academy of Sciences)

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