Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teaching focuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of teaching consistent, version space learners in an interactive setting. At any time step, the teacher provides an example, the learner performs an update, and the teacher observes the learner's new state. We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as the "worst-case" model (the learner picks the next hypothesis randomly from the version space) and the "preference-based" model (the learner picks hypothesis according to some global preference). Inspired by human teaching, we propose a new model where the learner picks hypotheses according to some local preference defined by the current hypothesis. We show that our model exhibits several desirable properties, e.g., adaptivity plays a key role, and the learner's transitions over hypotheses are smooth/interpretable. We develop adaptive teaching algorithms, and demonstrate our results via simulation and user studies.
This paper considers when knowing a learner's current hypothesis can lead to more effective teaching than if the hypothesis is not known. The benefits of adaptivity are explored in two cases where there is an impact, and algorithms are developed for adaptive teaching in those domains that attain efficient performance. The growth rate of the number of examples for adaptive and non-adaptive teachers is shown, and adaptive algorithms are compared to alternatives both in simulation and with human learners.  This paper addresses an important issue in algorithmic teaching, as adaptivity is often costly and understanding when adaptivity is important could be useful for building adaptive teaching systems. I appreciate that the paper made clear conceptually, as well as mathematically, when adaptivity matters. The paper focuses on local preference updating, often making reference to cases where the hypothesis does change but changes in a way that's dependent on the current hypothesis, but my reading is that any dependence on the current hypothesis has the potential to have adaptivity matter, including simply having the hypothesis stay the same if new data doesn't violate it. I bring up this case because it's consistent with some simple human learning models, such as the "win stay lose shift" model (e.g., Levine, M. (1975). A cognitive theory of learning: Research on hypothesis testing; Bonawitz, E., Denison, S., Gopnik, A., & Griffiths, T. L. (2014). Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference. Cognitive psychology, 74, 35-65; Rafferty, A. N., Brunskill, E., Griffiths, T. L., & Shafto, P. (2016). Faster teaching via POMDP planning. Cognitive science, 40(6), 1290-1332). It would be interesting to explore how much adaptivity is helpful with such a simple model that is less constrained in what happens when the current hypothesis is violated - the preference function \sigma is essentially local only when the current hypothesis is still valid. Making links to this type of work with human learning models could strengthen the impact of the present paper.  The main questions I had center on the human evaluation, where I believe the results are overstated. The "Real-world Human Preferences" uses data from participants to argue that people perform incremental updates when learning in 2-Rec. While I agree this is probably the case, the section doesn't acknowledge the (likely large) impact of the interface on these results. The participant has a hypothesis shown on screen at all times, and I believe participants were asked to "update" this based on the new information.  This interface constrains people to match the version space learner (at least in terms of what we can observe) by having exactly one hypothesis, and that hypothesis must be consistent with all data provided so far; while other representations are possible and have been frequently used for representing human category learning (e.g., maintaining an implicit distribution over possible hypotheses), these can't be expressed. The interface also nudges people towards local updates - e.g., I expect results would be at least somewhat different if people had to draw new rectangles every time, on a different image than the one with the hypothesis displayed (and probably more different still if they couldn't see the previously drawn hypothesis). Thus, the human evaluation seems to show that people are helped by an adaptive algorithm (as compared to random examples or set cover), given an interface that pushes them towards the learner model that is assumed by the adaptive algorithm. I still think this is important and useful to show, but the limitations of this evaluation should be acknowledged.  Additional questions about the human evaluation: - Why wasn't the non-adaptive algorithm (non-R) considered? That seems like it would be the natural comparison for understanding how much adaptivity matters in this case, and I would very much like to see results for the non-R case, more closely mirroring the simulated case. -  I did not understand the different between the target hypothesis being M_{1} versus M_{1/2} in Figure 3a. My understanding from the other cases where the subscript 1/2 is used is that it is for cases where either 1 or 2 rectangles could be consistent with the data. Is the distinction here that M_{1} is a single square (so non-representable using a 2 rectangle hypothesis), but M_{(1/2)} is a larger rectangle, which could be expressed by 1 rectangle or by 2 adjacent rectangles? If so, why this case is separated out for the human learners and not for the simulated evaluation? I may very well be missing something and clarification would be appreciated.  The consideration of noise in the preference function in the supplement was helpful in thinking about applications of this work. In the noisy case, why were only SC and Ada considered, not Non? As above, Ada versus Non seems like the most interesting comparison to see the impact of adaptivity with both adaptive and non adaptive having the (noisily) correct preference function. I look forward to the authors making the point about SC outperforming Non and incorporating the additional baseline, as discussed in their response.  To summarize based on the four review criteria: - Quality: The theoretical work here is sound, and the simulations support these theoretical results. The behavioral experiment is a key part of supporting the relevance of this work for human teaching, and it provides moderate support for the relevance of the theoretical work, but as discussed above, the limitations of the current design and how it drives people's behavior towards the proposed model are not adequately discussed. - Clarity: The paper is well-organized and understandable, with an appropriate level of detail in both the main paper and the supplementary materials. There are several individual points that were not clear (my questions are provided elsewere in this review), but as a whole, clarity is good. - Originality: To the best of my knowledge, framing the adaptivity question in this rigorous theoretical way is novel, and the work appropriately cites related work and argues for why this work adds to the existing literature. - Significance: The paper goes beyond previous work, exploring the importance of adaptivity from three methodologies (theoretical, simulation, and behavioral experiments), and is likely to be of interest both to those interested specifically in the theoretical results for machine teaching and those who are more oriented towards educational applications. Other researchers in machine teaching are likely to build on this work, and the rigorous approach of determining when adaptivity makes teaching/learning more efficient is likely to be relevant to applied work in using machine learning approaches for education.  After reading the author response and the other reviews, my general evaluation of the paper remains the same. The response clarifies several questions, and the proposed revisions will, I believe, increase the clarity of the paper. While computing the optimal teaching policy is hard (and that problem is not solved by this work), I think it is an important contribution to formalize the problem in this way, and will perhaps facilitate future work that focuses on techniques for the optimization problem. I'm unsure from the response if the new experimental study is intended to be included in this work or in future work; if the latter, I again want to strongly urge the authors to note the limitation of how the present interface may encourage participants towards the assumed learner model.  Small issues/typos: - Line 10: "learner picks hypothesis according to..." -> hypotheses - Line 241: "poorly.An" - Line 329: "an web-interface" - Line 348: "There were randomly assigned" - Caption Figure 3: "require less examples" -> "require fewer examples" - Line 397 in the supplement: "100 participants on Mechanical Turk were each shown the same set of then initial configurations." -> ten, not then