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1. Adaptive Sampling for Discovery}

   Xu, Ziping; Shim, Eunjae; Tewari, Ambuj; Zimmerman, Paul (December 2022, Advances in Neural Information Processing Systems 35)
2. Boson sampling for generalized bosons

   https://doi.org/10.1103/PhysRevResearch.4.043096  

   Kuo, En-Jui; Xu, Yijia; Hangleiter, Dominik; Grankin, Andrey; Hafezi, Mohammad (November 2022, Physical Review Research)
3. Graph Sampling for Map Comparison

   https://doi.org/10.1145/3662733  

   Aguilar, Jordi; Buchin, Kevin; Buchin, Maike; Hosseini_Sereshgi, Erfan; Silveira, Rodrigo I; Wenk, Carola (May 2024, ACM Transactions on Spatial Algorithms and Systems)

   Comparing two road maps is a basic operation that arises in a variety of situations. A map comparison method that is commonly used, mainly in the context of comparing reconstructed maps to ground truth maps, is based ongraph sampling. The essential idea is to first compute a set of point samples on each map, and then to match pairs of samples—one from each map—in a one-to-one fashion. For deciding whether two samples can be matched, different criteria, e.g., based on distance or orientation, can be used. The total number of matched pairs gives a measure of how similar the maps are. Since the work of Biagioni and Eriksson [11, 12], graph sampling methods have become widely used. However, there are different ways to implement each of the steps, which can lead to significant differences in the results. This means that conclusions drawn from different studies that seemingly use the same comparison method, cannot necessarily be compared. In this work we present a unified approach to graph sampling for map comparison. We present the method in full generality, discussing the main decisions involved in its implementation. In particular, we point out the importance of the sampling method (GEO vs. TOPO) and that of the matching definition, discussing the main options used, and proposing alternatives for both key steps. We experimentally evaluate the different sampling and matching options considered on map datasets and reconstructed maps. Furthermore, we provide a code base and an interactive visualization tool to set a standard for future evaluations in the field of map construction and map comparison. 

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4. Variational approximation for importance sampling

   https://doi.org/10.1007/s00180-021-01063-w  

   Su, Xiao; Chen, Yuguo (January 2021, Computational Statistics)
5. Information sampling for contingency planning.

   Ma, I.; Ma, W.J.; Gureckis, T.M. (January 2021, Proceedings of the 43rd Annual Conference of the Cognitive Science Society)

   null (Ed.)

   From navigation in unfamiliar environments to career plan- ning, people typically first sample information before com- mitting to a plan. However, most studies find that people adopt myopic strategies when sampling information. Here we challenge those findings by investigating whether contingency planning is a driver of information sampling. To this aim, we developed a novel navigation task that is a shortest path find- ing problem under uncertainty of bridge closures. Participants (n = 109) were allowed to sample information on bridge sta- tuses prior to committing to a path. We developed a computa- tional model in which the agent samples information based on the cost of switching to a contingency plan. We find that this model fits human behavior well and is qualitatively similar to the approximated optimal solution. Together, this suggests that humans use contingency planning as a driver of information sampling. 

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