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Vector-Space Markov Random Fields via Exponential Families.

, , , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 684-692. JMLR.org, (2015)

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Multiagent Learning through Neuroevolution., , , , , , and . WCCI, volume 7311 of Lecture Notes in Computer Science, page 24-46. Springer, (2012)Diet2Vec: Multi-scale analysis of massive dietary data., , and . CoRR, (2016)Selected Papers from the Workshop on Computational Biology: Joint with the International Joint Conference on Artificial Intelligence and the International Conference on Machine Learning, 2018., , , , , and . J. Comput. Biol., 26 (6): 507-508 (2019)Interpreting Black Box Models via Hypothesis Testing., , and . FODS, page 47-57. ACM, (2020)Maximum-Variance Total Variation Denoising for Interpretable Spatial Smoothing., , and . AAAI, page 2460-2467. AAAI Press, (2018)Accelerating evolution via egalitarian social learning., , and . GECCO, page 919-926. ACM, (2012)Targeted active learning for probabilistic models., , and . CoRR, (2022)DIET: Conditional independence testing with marginal dependence measures of residual information., , , and . CoRR, (2022)Interpreting Black Box Models with Statistical Guarantees., , and . CoRR, (2019)Quantile regression with ReLU Networks: Estimators and minimax rates., , and . J. Mach. Learn. Res., (2022)