Paper ID: | 2701 |
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Title: | Cost Effective Active Search |

The paper considers a Bayesian decision theoretic formulation of the problem of minimizing the number of queries to identify the desired number of positive instances (instances with positive labels), given a probabilistic model of the labels in the dataset. This formulation is motivated by the material and drug discovery problems. The problem is properly formulated and contrasted with the recently suggested budgeted-learning setting, where the goal is to identify the largest number of positive instances given a fixed budget on queries. Further the authors show that the optimal Bayesian policy is hard to compute and hard to approximate. However, further assuming certain conditional independence the policy can be approximated efficiently using the negative-poisson-binomial distribution, for which the authors propose computationally-cheap expectation estimates.The resulting policy is compared to several other alternatives, and it is shown to obtain overall superior performance in both material discovery and drug discovery datasets. Quality: Most of the material presented in the paper is sound and the experimental results are convincing. The proof for the main theoretical statement is provided in the supplementary material, but I didn’t check it. There are however some gaps that are not completely satisfactory in the main text. Specifically, it is about the approximation of the expected value of the NPB distribution discussed in section 4.2. A single set of experimental results suggesting that the proposed estimator ACCU’ is accurate is insufficient in my opinion to use, given that its accuracy is central to the policy choice. Obtaining some sort of bounds is important. On the other hand, it is also not completely clear to me why the \epsilon-DP is computationally expensive in the first place. After all, a single evaluation of the instance is assumed to be expensive (time, resources, etc.), so spending more time to accurately getting the policy should not be a bottleneck. Another option would be estimating the expected value by sampling. There are lots of exponential tail bounds that give solid guarantees, wouldn’t this work? Regarding the experiments, I don’t see enough information about the details of the posterior probability model, beyond a short mentioning that it is based on a k-nn. A detailed model should be explained somewhere in the text (main or supplementary). Furthermore, there are no details regarding how the ENS is modified to match the CAES setting in the experimental setup. This is particularly interesting as it is important to know if there are relatively easy ways to map one problem to the other, despite the opposing arguments put forward in the introduction. On that note, I would suggest to revise this argument (the third paragraph in the into). Why is the problem not one-to-one? Does it even matter? Originality: As far as I know this work is original, starting from the suitable problem formulation and down to the algorithmic implementation. It certainly adapts ideas from prior work but they are sufficiently different. Clarity: The paper is overall well written. Significance: I believe that the work presented is sufficiently significant and will be beneficial for suitable search problems that are not very unique in the general scientific community. It provides a new problem formulation that is more suitable to those search problems, the algorithms are implemented, discussed and compared on two interesting real datasets. Also, the difficulty of approximating an optimal Bayesian policy for a general posterior model is an important (though not surprising) theoretical result. Misc: Line 129: What is “n” in O(n^t)?

*Originality* The abstract claims that the current submission is the first one to systematically model the active search problem as a Bayesian formulation and the resulting algorithm outscores prior work. However, the theoretical model in paper is very limited. The authors assume a fixed prior and derive a Bellman equation. There is some novelty in the choice of the state and to include number of queries remaining in the state, but these are all standard tricks in the MDP literature. Furthermore, at the end the algorithm presented is a heuristic one. The authors assume -- as the prior work -- that the distribution of the state of their MDP does not change with the outcomes of the currently considered points. This decoupling assumption relieves the algorithm of the complexity of computing these posteriors for each point at each step. They simply assume a fixed distribution for the state with updated parameters. Specifically, they assume a negative Poisson Binomial distribution (which they have defined in the paper and is perhaps new). This is itself a heuristic and places the current submission at par with prior heuristic search methods. *Quality* For me, this work is mainly experimental. The authors have not compared with the large body of theoretical work on active search, which perhaps does not lead to algorithms as efficient as the authors desire, but is theoretically more rich. The authors should have at least clarified why that literature is not relevant here. Further, as described above, the only theory developed in the paper is the Bellman equation for an appropriate state function, which is interesting but not comprehensive. Thus, I think the main contribution of the paper is a heuristic algorithm which fares better than some prior work on specific data sets. For experiments, the authors considered two use-cases of drug discovery and material discovery and compared many existing methods. However, I found the improvement reported in Figure 1(b) rather marginal. But the numbers reported in Table 2 are perhaps acceptable as a benchmark for convincing numerical improvement. I am not an expert on this point. *Clarity* The presentation is very confusing. Even the problem formulation was never concretely laid down and it took me several readings to reconstruct the problem. What Bayesian formulation are the authors referring to? Is the the knowledge of the exact posterior assumed or only within a family? Is the cost the expected number of queries or you want a confidence bound? All these questions are eventually clarified but never explicitly. I would have preferred a complete formulation at the outset. Also, it was never clarified which distribution (what parameters for NPB) are used in the first step of iteration. I understand the posterior updates from there on, but what do you start with? I assume all p_is are set as 1/2 first. Overall, I think it is an interesting paper but below par for NeurIPS publication. [Update after rebuttal] The authors have not really clarified my two important points: How is their assumption of distribution justified (is it the least-favorable one and matches some minimax lower bound?) and how is their decoupling assumption justified? However, reading other reviews I feel that these are not the main concerns for people working in this area. So, I can't increase the score, but I can reduce my level of confidence to 2. Perhaps the area chair can take a look and decide.

In this paper the authors investigate the following cost-effective active search problem: the goal is to find a given number of positive points which minimize the label cost. Here they used the Bayesian decision model. They show a strong hardness result: any algorithm with complexity O(n^{n^0.16}) cannot approximate the optimal cost with in \Omega(n^0.16). Previously there is an upper bound O(1/\sqrt{log n}) for the maximization version of the problem. They also give an approximation algorithm for the minimization problem as well as some experiment under different scenarios. Here are some detailed comments: The author claim that it results in an exponentially stronger bound (n^0.16 vs \sqrt{log n}). (Line 147) However, the two problem are different, one is minimization problem, one is maximization problem. There is no necessary connection between the approximation ratio for the min and the max problem. For example, for set cover problem, the approximation ratio for minimization version is O(log n), but the approximation for maximization version is 1-1/e. For the hardness result, what is the dependent on the parameter T? Because when T=O(1), there is some DP algorithm which can solve the problem in polynomial time. Line 110, “Let p(x)=p(y=1:x,D_i)”, here the notation of p(x) does not show the dependence on index i

The paper is well structured and is easy to follow for the most part. The authors proposed to approximate the expected remaining cost via the expectation of a negative Poisson binomial (NPB) distribution. The proposed nonmyopic algorithm, ENCES, aims at minimizing the approximated expectation of the corresponding NPB distribution. Note that this is an approximation because the conditional independence (CI) assumption does not always hold. Can the authors please justify, either empirically (on small toy dataset) or theoretically, how well the NPB expectation approximate the true expected cost? In addition to the query complexity, it will be useful to see the time/computational complexity of the competing algorithms, e.g., how much more expensive is ENCES than the (one-step) greedy algorithm? == post-rebuttal comments: Thanks a lot for the clarification on the conditional independence (CI) assumption. One comparison I would like to make is against variational inference, which also makes independence assumption for the approximate distribution -- however, VI tries to find the closest approximation in a parameterized family. In contrast, here the conditional independence may not hold and hence it's hard to provide any guarantees on the performance based on the proposed search heuristics. I think this is still a reasonable heuristic to run (and a good one given the experimental results), while there is still room for improvement on the theoretical aspects.