More Like this

1. Accounting for matching structure in post-matching analysis of observational studies

   https://doi.org/10.1080/03610918.2019.1708928  

   Zhang, Yuyang; Lu, Bo (June 2022, Communications in Statistics - Simulation and Computation)
2. 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)
3. Provable Benefits of Score Matching

   Pabbaraju, C; Rohatgi, D; Sevekari, A; Lee, H; Moitra, A; Risteski, A. (December 2023, Conference on Neural Information Processing Systems (NeurIPS), 2023)

   Score matching is an alternative to maximum likelihood (ML) for estimating a probability distribution parametrized up to a constant of proportionality. By fitting the ''score'' of the distribution, it sidesteps the need to compute this constant of proportionality (which is often intractable). While score matching and variants thereof are popular in practice, precise theoretical understanding of the benefits and tradeoffs with maximum likelihood---both computational and statistical---are not well understood. In this work, we give the first example of a natural exponential family of distributions such that the score matching loss is computationally efficient to optimize, and has a comparable statistical efficiency to ML, while the ML loss is intractable to optimize using a gradient-based method. The family consists of exponentials of polynomials of fixed degree, and our result can be viewed as a continuous analogue of recent developments in the discrete setting. Precisely, we show: (1) Designing a zeroth-order or first-order oracle for optimizing the maximum likelihood loss is NP-hard. (2) Maximum likelihood has a statistical efficiency polynomial in the ambient dimension and the radius of the parameters of the family. (3) Minimizing the score matching loss is both computationally and statistically efficient, with complexity polynomial in the ambient dimension. 

   more » « less
4. 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)
5. 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. 

   more » « less