Tom Griffith: This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science. The first section mentions several useful general references, and the others provide supplementary readings on specific topics. If you would like to suggest some additions to the list, contact Tom Griffiths.
Bayesian probability is an interpretation of probability suggested by Bayesian theory, which holds that the concept of probability can be defined as the degree to which a person believes a proposition. Bayesian theory also suggests that Bayes' theorem can
A. Souza, L. Nardi, L. Oliveira, K. Olukotun, M. Lindauer, und F. Hutter. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), (September 2021)To appear.
J. Huggins, M. Kasprzak, T. Campbell, und T. Broderick. (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.
F. Lemmerich, M. Becker, P. Singer, D. Helic, A. Hotho, und M. Strohmaier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, Seite 965--974. ACM, (2016)
A. Kendall, und Y. Gal. Proceedings of the 31st International Conference on Neural Information Processing Systems, Seite 5580–5590. Red Hook, NY, USA, Curran Associates Inc., (2017)
K. Shridhar, F. Laumann, und M. Liwicki. (2019)cite arxiv:1901.02731Comment: arXiv admin note: text overlap with arXiv:1506.02158, arXiv:1703.04977 by other authors.
M. Vadera, A. Cobb, B. Jalaian, und B. Marlin. (2020)cite arxiv:2007.04466Comment: Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
J. Hron, A. Matthews, und Z. Ghahramani. Proceedings of the 35th International Conference on Machine Learning, Volume 80 von Proceedings of Machine Learning Research, Seite 2019--2028. Stockholmsmässan, Stockholm Sweden, PMLR, (10--15 Jul 2018)
C. Chu, K. Minami, und K. Fukumizu. (2020)cite arxiv:2004.01822Comment: ICLR 2020, Workshop on Integration of Deep Neural Models and Differential Equations.
S. Sinha, H. Bharadhwaj, A. Goyal, H. Larochelle, A. Garg, und F. Shkurti. (2020)cite arxiv:2003.04514Comment: Samarth Sinha* and Homanga Bharadhwaj* contributed equally to this work. Code will be released at https://github.com/rvl-lab-utoronto/dibs.