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publications
Visualizing Uncertainty in Predictive Models
Published in Scientific Visualization, 2014
Recommended citation: Penny Rheingans, Marie desJardins, Wallace Brown, Alex Morrow, Doug Stull, and Kevin Winner. Visualizing Uncertainty in Predictive Models, pages 61–69. Springer London, London, 2014 https://doi.org/10.1007/978-1-4471-6497-5_6
Inference in a partially observed queueing model with applications in ecology
Published in AAAI 2015 Workshop on Computational Sustainability, 2015
Recommended citation: Kevin Winner and Daniel Sheldon. Inference in a partially observed queueing model with applications in ecology. In AAAI 2015 Workshop on Computational Sustainability, 2015
Inference in a partially observed queueing model with applications in ecology
Published in ICML, 2015
Recommended citation: Kevin Winner, Garrett Bernstein, and Daniel Sheldon. Inference in a partially observed queueing model with applications in ecology. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pages 2512-2520. JMLR Workshop and Conference Proceedings, 2015 http://kwinner.github.io/files/WinnerBernsteinSheldon2015.pdf
Migration timing and its determinants for nocturnal migratory birds during autumn migration
Published in Journal of Animal Ecology, 2015
Recommended citation: Frank A. La Sorte, Wesley M. Hochachka, Andrew Farnsworth, Daniel Sheldon, Daniel Fink, Jeffrey Geevarghese, Kevin Winner, Benjamin M. Van Doren, and Steve Kelling. (2015), Migration timing and its determinants for nocturnal migratory birds during autumn migration. Journal of Animal Ecology, 84(5):1202–1212, 2015 http://dx.doi.org/10.1111/1365-2656.12376
A characterization of autumn nocturnal migration detected by weather surveillance radars in the northeastern USA
Published in Ecological Applications, 2016
Recommended citation: Andrew Farnsworth, Benjamin Mark Van Doren, Wesley M. Hochachka, Daniel Sheldon, Kevin Winner, Jed Irvine, Jeffrey Geevarghese, and Steve Kelling. A characterization of autumn nocturnal migration detected by weather surveillance radars in the northeastern US. Ecological Applications, 26(3):752–770, 2016 http://dx.doi.org/10.1890/15-0023
Probabilistic inference with generating functions for Poisson latent variable models
Published in NIPS, 2016
Recommended citation: Kevin Winner and Daniel Sheldon. Probabilistic inference with generating functions for Poisson latent variable models. In Advances in Neural Information Processing Systems 30, 2016 http://kwinner.github.io/files/WinnerSheldon2016.pdf
Exact inference for integer latent-variable models
Published in ICML, 2017
Discrete latent variables with infinite support pose a significant challenge for existing inference techniques. In this paper we propose the first known inference algorithms for models with integer-valued latent variables in the context of population ecology.
Recommended citation: Kevin Winner, Debora Sujono, and Daniel Sheldon. Exact inference for integer latent-variable models. In Proceedings of the 34th International Conference on Machine Learning - Volume 39, 2017 http://kwinner.github.io/files/WinnerSujonoSheldon2017.pdf
Statistical inference for home range overlap
Published in MEE, 2018
Recommended citation: Kevin Winner, Michael J. Noonan, Chris H. Fleming, Kirk Olson, Thomas Mueller, Dan Sheldon, and Justin M. Calabrese. Statistical inference for home range overlap. Methods in Ecology and Evolution, 2018 http://kwinner.github.io/files/Winneretal2018.pdf
Learning in integer latent variable models with nested automatic differentiation
Published in ICML, 2018
Recommended citation: Daniel Sheldon, Kevin Winner, and Debora Sujono. Learning in integer latent variable models with nested automatic differentiation. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, July 10-15, 2018, pages 4622–4630, 2018 https://people.cs.umass.edu/~sheldon/papers/pgf-backprop.pdf