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   Heath, Katy D.; Grillo, Michael A. (December 2016, Environmental Microbiology)
2. Minimal models for altermagnetism

   https://doi.org/10.1103/PhysRevB.110.144412  

   Roig, Mercè; Kreisel, Andreas; Yu, Yue; Andersen, Brian_M; Agterberg, Daniel_F (October 2024, Physical Review B)
3. Better null models for assessing predictive accuracy of disease models

   https://doi.org/10.1371/journal.pone.0285215  

   Keyel, Alexander C.; Kilpatrick, A. Marm (May 2023, PLOS ONE)

   Ansari, Ali R. (Ed.)

   Null models provide a critical baseline for the evaluation of predictive disease models. Many studies consider only the grand mean null model (i.e. R 2 ) when evaluating the predictive ability of a model, which is insufficient to convey the predictive power of a model. We evaluated ten null models for human cases of West Nile virus (WNV), a zoonotic mosquito-borne disease introduced to the United States in 1999. The Negative Binomial, Historical (i.e. using previous cases to predict future cases) and Always Absent null models were the strongest overall, and the majority of null models significantly outperformed the grand mean. The length of the training timeseries increased the performance of most null models in US counties where WNV cases were frequent, but improvements were similar for most null models, so relative scores remained unchanged. We argue that a combination of null models is needed to evaluate the forecasting performance of predictive models for infectious diseases and the grand mean is the lowest bar. 

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4. Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise

   https://doi.org/10.1109/CAMSAP45676.2019.9022612  

   Mathews, Heather; Mayya, Vaishakhi; Volfovsky, Alexander; Reeves, Galen (December 2019, 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP))

   Community detection tasks have received a lot of attention across statistics, machine learning, and information theory with work concentrating on providing theoretical guarantees for different methodological approaches to the stochastic block model. Recent work on community detection has focused on modeling the spectral embedding of a network using Gaussian mixture models (GMMs) in scaling regimes where the ability to detect community memberships improves with the size of the network. However, these regimes are not very realistic. This paper provides tractable methodology motivated by new theoretical results for networks with non-vanishing noise. We present a procedure for community detection using novel GMMs that incorporate truncation and shrinkage effects. We provide empirical validation of this new representation as well as experimental results using a large email dataset. 

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5. Lower bounds for testing graphical models: colorings and antiferromagnetic Ising models

   Bezakova, Ivona; Blanca, Antonio; Chen, Zongchen; Stefankovic, Daniel; Vigoda, Eric (July 2019, Proceedings of Machine Learning Research)

   We study the identity testing problem in the context of spin systems or undirected graphical models, where it takes the following form: given the parameter specification of the model M and a sampling oracle for the distribution \mu_{M^*} of an unknown model M^*, can we efficiently determine if the two models M and M^* are the same? We consider identity testing for both soft-constraint and hard-constraint systems. In particular, we prove hardness results in two prototypical cases, the Ising model and proper colorings, and explore whether identity testing is any easier than structure learning. For the ferromagnetic (attractive) Ising model, Daskalasis et al. (2018) presented a polynomial time algorithm for identity testing. We prove hardness results in the antiferromagnetic (repulsive) setting in the same regime of parameters where structure learning is known to require a super-polynomial number of samples. In particular, for n-vertex graphs of maximum degree d, we prove that if |\beta| d = \omega(\log n) (where \beta is the inverse temperature parameter), then there is no identity testing algorithm for the antiferromagnetic Ising model that runs in polynomial time unless RP = NP. We also establish computational lower bounds for a broader set of parameters under the (randomized) exponential time hypothesis. In our proofs, we use random graphs as gadgets; this is inspired by similar constructions in seminal works on the hardness of approximate counting. In the hard-constraint setting, we present hardness results for identity testing for proper colorings. Our results are based on the presumed hardness of #BIS, the problem of (approximately) counting independent sets in bipartite graphs. In particular, we prove that identity testing for colorings is hard in the same range of parameters where structure learning is known to be hard, which in turn matches the parameter regime for NP-hardness of the corresponding decision problem. 

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