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1. Matching soulmates

   https://doi.org/10.1111/jpet.12542  

   Leo, Greg; Lou, Jian; Van der Linden, Martin; Vorobeychik, Yevgeniy; Wooders, Myrna (October 2021, Journal of Public Economic Theory)
2. Matching Composition and Efficient Weight Reduction in Dynamic Matching

   Bernstein, Aaron; Chen, Jiale; Dudeja, Aditi; Langley, Zachary; Sidford, Aaron; Tu, Ta-Wei (January 2025, Society for Industrial and Applied Mathematics)
3. Complexity matching and lexical matching in monolingual and bilingual conversations

   https://doi.org/10.1017/S1366728919000774  

   Schneider, S; Ramirez-Aristizabal, A; Gavilan, C; Kello, C.T. (January 2020, Bilingualism)

   When people interact, aspects of their speech and language patterns often converge in inter- actions involving one or more languages. Most studies of speech convergence in conversations have examined monolingual interactions, whereas most studies of bilingual speech conver- gence have examined spoken responses to prompts. However, it is not uncommon in multi- lingual communities to converse in two languages, where each speaker primarily produces only one of the two languages. The present study examined complexity matching and lexical matching as two measures of speech convergence in conversations spoken in English, Spanish, or both languages. Complexity matching measured convergence in the hierarchical timing of speech, and lexical matching measured convergence in the frequency distributions of lemmas produced. Both types of matching were found equally in all three language conditions. Taken together, the results indicate that convergence is robust to monolingual and bilingual interac- tions because it stems from basic mechanisms of coordination and communication. 

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4. CEGMA: Coordinated Elastic Graph Matching Acceleration for Graph Matching Networks

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5. Matching While Learning

   https://doi.org/10.1287/opre.2020.2013  

   Johari, Ramesh; Kamble, Vijay; Kanoria, Yash (March 2021, Operations Research)

   null (Ed.)

   We consider the problem faced by a service platform that needs to match limited supply with demand while learning the attributes of new users to match them better in the future. We introduce a benchmark model with heterogeneous workers (demand) and a limited supply of jobs that arrive over time. Job types are known to the platform, but worker types are unknown and must be learned by observing match outcomes. Workers depart after performing a certain number of jobs. The expected payoff from a match depends on the pair of types, and the goal is to maximize the steady-state rate of accumulation of payoff. Although we use terminology inspired by labor markets, our framework applies more broadly to platforms where a limited supply of heterogeneous products is matched to users over time. Our main contribution is a complete characterization of the structure of the optimal policy in the limit that each worker performs many jobs. The platform faces a tradeoff for each worker between myopically maximizing payoffs (exploitation) and learning the type of the worker (exploration). This creates a multitude of multiarmed bandit problems, one for each worker, coupled together by the constraint on availability of jobs of different types (capacity constraints). We find that the platform should estimate a shadow price for each job type and use the payoffs adjusted by these prices first to determine its learning goals and then for each worker (i) to balance learning with payoffs during the exploration phase and (ii) to myopically match after it has achieved its learning goals during the exploitation phase. 

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