Efficient nonmyopic batch active search
Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In these settings, specialized instruments can often evaluate multiple points simultaneously; however, all existing work on active search focuses on sequential acquisition. We bridge this gap, addressing batch active search from both the theoretical and practical perspective. We first derive the Bayesian optimal policy for this problem, then prove a lower bound on the performance gap between sequential and batch optimal policies: the "cost of parallelization." We also propose novel, efficient batch policies inspired by state-of-the-art sequential policies, and develop an aggressive pruning technique that can dramatically speed up computation. We conduct thorough experiments on data from three application domains: a citation network, material science, and drug discovery, testing all proposed policies with a wide range of batch sizes. Our results demonstrate that the empirical performance gap matches our theoretical bound, that nonmyopic policies usually significantly outperform myopic alternatives, and that diversity is an important consideration for batch policy design.
This work investigates the different batch-mode extensions of an active search method called efficient nonmyopic policy (ENS) [12]. ENS achieves good performance efficiently because it assumes the sample selections are independent after a step [12]. This paper proposes two strategies: 1) converting the batch active search problem to sequential one by guessing the hidden labels of selected samples 2) try to enumerate all possible hidden labels of selected samples by Monte Carlo.  Strength:  Allowing to sample batches is important in practice. The work addresses several theoretical and practical challenges in many aspects of batch active search such as how difficult batch active search could be, why pessimistic oracle works well, and how to make the methods more efficient by pruning. The explanation is clear, and the literature review is comprehensive. The experiment results seem to show significant improvement as well.  Weakness:  The ideas of extension seem to be intuitive and not very novel (the authors seem to honestly admit this in the related work section when comparing this work with [3,8,9]). This seems to make the work a little bit incremental. In the experiments, Monte Carlo (batch-ENS) works pretty well consistently, but the authors do not provide intuitions or theoretical guarantees to explain the reasons.    Questions: 1. In [12], they also show the results of GpiDAPH3 fingerprint. Why not also run the experiment here? 2. You said only 10 out of 120 datasets are considered as in [7,12]. Why not compare batch and greedy in other 110 datasets? 3. If you change the budget (T) in the drug dataset, does the performance decay curve still fits the conclusion of Theorem 1 well (like Figure 1(a))? 4. In the material science dataset, the pessimistic oracle seems not to work well. Why do your explanations in Section 5.2 not hold in the dataset?    Suggestions: Instead of just saying that the drug dataset fits Theorem 1 well, it will be better to characterize the properties of datasets to which you can apply Theorem 1 and your analysis shows that this drug dataset satisfies the properties, which naturally implies Theorem 1 hold and demonstrate the practical value of Theorem 1.  Minor suggestions: 1. Equation (1): Using X to denote the candidate of the next batch is confusing because it is usually used to represent the set of all training examples 2. In the drug dataset experiment, I cannot find how large the budget T is set 3. In section 5.2, the comparison of myopic vs. nonmyopic is not necessary. The comparison in drug dataset has been done at [12].  In supplmentary material 4. Table 1 and 2: why not also show results when batch size is 1 as you did in the drug dataset? 5. In the material science dataset experiment, I cannot find how large the budget T is set  After rebuttal: Thanks for the explanation. It is nice to see the theorem roughly holds for the batch size part when different budgets are used. However, based on this new figure, the performance does not improve with the rate 1/log(T) as T increases. I suggest authors to replace Figure 1a with the figure in the rebuttal and address the possible reasons (or leave it as future work) of why the rate 1/log(T) is not applied here. There are no major issues found by other reviewers, so I changed my rate from tending to accept to accepting.