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1. Multi-Item Mechanisms without Item-Independence: Learnability via Robustness

   https://doi.org/10.1145/3391403.3399541  

   Brustle, Johannes; Cai, Yang; Daskalakis, Constantinos (July 2020, 21st ACM Conference on Economics and Computation)

   We study the sample complexity of learning revenue-optimal multi-item auctions. We obtain the first set of positive results that go beyond the standard but unrealistic setting of item-independence. In particular, we consider settings where bidders' valuations are drawn from correlated distributions that can be captured by Markov Random Fields or Bayesian Networks -- two of the most prominent graphical models. We establish parametrized sample complexity bounds for learning an up-to-ε optimal mechanism in both models, which scale polynomially in the size of the model, i.e. the number of items and bidders, and only exponential in the natural complexity measure of the model, namely either the largest in-degree (for Bayesian Networks) or the size of the largest hyper-edge (for Markov Random Fields). We obtain our learnability results through a novel and modular framework that involves first proving a robustness theorem. We show that, given only "approximate distributions" for bidder valuations, we can learn a mechanism whose revenue is nearly optimal simultaneously for all "true distributions" that are close to the ones we were given in Prokhorov distance. Thus, to learn a good mechanism, it suffices to learn approximate distributions. When item values are independent, learning in Prokhorov distance is immediate, hence our framework directly implies the main result of Gonczarowski and Weinberg. When item values are sampled from more general graphical models, we combine our robustness theorem with novel sample complexity results for learning Markov Random Fields or Bayesian Networks in Prokhorov distance, which may be of independent interest. Finally, in the single-item case, our robustness result can be strengthened to hold under an even weaker distribution distance, the Levy distance. 

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2. Investigating Item Bias in a CS1 Exam with Differential Item Functioning

   https://doi.org/10.1145/3408877.3432397  

   Davidson, Matt J.; Wortzman, Brett; Ko, Amy J.; Li, Min (March 2021, ACM Technical Symposium on Computer Science Education (SIGCSE))

   null (Ed.)
3. Controllable Gradient Item Retrieval

   https://doi.org/10.1145/3442381.3449963  

   Wang, Haonan; Zhou, Chang; Yang, Carl; Yang, Hongxia; He, Jingrui (April 2021, The Web Conference 2021)

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4. Mapping Unobserved Item–Respondent Interactions: A Latent Space Item Response Model with Interaction Map

   https://doi.org/10.1007/s11336-021-09762-5  

   Jeon, Minjeong; Jin, Ick Hoon; Schweinberger, Michael; Baugh, Samuel (January 2021, Psychometrika)

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   Classic item response models assume that all items with the same difficulty have the same response probability among all respondents with the same ability. These assumptions, however, may very well be violated in practice, and it is not straightforward to assess whether these assumptions are violated, because neither the abilities of respondents nor the difficulties of items are observed. An example is an educational assessment where unobserved heterogeneity is present, arising from unobserved variables such as cultural background and upbringing of students, the quality of mentorship and other forms of emotional and professional support received by students, and other unobserved variables that may affect response probabilities. To address such violations of assumptions, we introduce a novel latent space model which assumes that both items and respondents are embedded in an unobserved metric space, with the probability of a correct response decreasing as a function of the distance between the respondent’s and the item’s position in the latent space. The resulting latent space approach provides an interaction map that represents interactions of respondents and items, and helps derive insightful diagnostic information on items as well as respondents. In practice, such interaction maps enable teachers to detect students from underrepresented groups who need more support than other students. We provide empirical evidence to demonstrate the usefulness of the proposed latent space approach, along with simulation results. 

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5. The Impact of Adding a Fourth Item to the Traditional 3-Item Remote Associates Test

   https://doi.org/10.1080/10400419.2023.2200597  

   Diaz, Jose A; Nelson, Steven M; Beaujean, A Alexander; Green, Adam E; Scullin, Michael K (April 2023, Creativity Research Journal)