Active learning is increasingly adopted for expensive multi-objective
combinatorial optimization problems, but it involves a challenging subset
selection problem, optimizing the batch acquisition score that quantifies the
goodness of a batch for evaluation. Due to the excessively large search space
of the subset selection problem, prior methods optimize the batch acquisition
on the latent space, which has discrepancies with the actual space, or optimize
individual acquisition scores without considering the dependencies among
candidates in a batch instead of directly optimizing the batch acquisition. To
manage the vast search space, a simple and effective approach is the greedy
method, which decomposes the problem into smaller subproblems, yet it has
difficulty in parallelization since each subproblem depends on the outcome from
the previous ones. To this end, we introduce a novel greedy-style subset
selection algorithm that optimizes batch acquisition directly on the
combinatorial space by sequential greedy sampling from the greedy policy,
specifically trained to address all greedy subproblems concurrently. Notably,
our experiments on the red fluorescent proteins design task show that our
proposed method achieves the baseline performance in 1.69x fewer queries,
demonstrating its efficiency.

在昂贵的多目标组合优化问题中，积极学习被越来越多地采用，但它涉及一个具有挑战性的子集选择问题，即优化批量采集评估的好坏量化指标。为了管理庞大的搜索空间，我们引入了一种新颖的贪婪式子集选择算法，通过基于贪婪策略的顺序贪婪采样，直接在组合空间上优化批量采集，以同时解决所有贪婪子问题。值得注意的是，我们在红色荧光蛋白设计任务上的实验证明，我们提出的方法在查询次数减少了 1.69 倍的情况下，实现了基准性能，显示出其高效性。