[15] Thomas M Cover and Joy A Thomas. Elements of information theory. John Wiley & Sons, 2012.
[16] Jason V Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S Dhillon. Information-theoretic metric learning. In Proceedings of the 24th international conference on Machine learning, pages 209-216, 2007.
[17] Philippe Donnat, Gautier Marti, and Philippe Very. Toward a generic representation of random variables for machine learning. Pattern Recogn. Lett., 70(C):24-31, January 2016.
[18] J. . Durrieu, J. . Thiran, and F. Kelly. Lower and upper bounds for approximation of the Kullback-Leibler divergence between gaussian mixture models. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4833-4836, 2012.
[19] Lsj Farmer. The princeton companion to applied mathematics. Reference Reviews, 30(5):34-35, 2016.
[20] Sarah Filippi, Olivier Cappé, and Aurélien Garivier. Optimism in reinforcement learning and kullback-leibler divergence. In 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 115-122, 2010.
[21] Bela A. Frigyik, Santosh Srivastava, and Maya R. Gupta. Functional Bregman divergence. In 2008 IEEE International Symposium on Information Theory, pages 1681-1685, 2008.
[22] Abhik Ghosh and Ayanendranath Basu. A new family of divergences originating from model adequacy tests and application to robust statistical inference. IEEE Transactions on Information Theory, 64(8):5581-5591, 2018.
[23] Ian Goodfellow, *Yoshua Bengio*, Aaron Courville, and *Yoshua Bengio*. *Deep learning*, volume 1. MIT press Cambridge, 2016.
[24] Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola. A kernel two-sample test. Journal of Machine Learning Research, 13(25):723-773, 2012.
[25] Peng Guan, Maxim Raginsky, and Rebecca M. Willett. Online Markov decision processes with Kullback-Leibler control cost. IEEE Transactions on Automatic Control, 59(6):1423-1438, 2014.
[26] Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. Improved training of Wasserstein GANs. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, page 5769-5779, Red Hook, NY, USA, 2017. Curran Associates Inc.
[27] John R Hershey and Peder A Olsen. Approximating the Kullback-Leibler divergence between gaussian mixture models. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07, volume 4, pages IV-317. IEEE, 2007.
[28] Dihong Jiang, Sun Sun, and Yaoliang Yu. Revisiting flow generative models for out-of-distribution detection. In International Conference on Learning Representations, 2022.
[29] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems, pages 10215-10224, 2018.
[30] Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson. Why normalizing flows fail to detect out-of-distribution data. ICML workshop on Invertible Neural Networks and Normalizing Flows, 2020 (NeurIPS 2020), 2020.
[31] Solomon Kullback. Information theory and statistics. Courier Corporation, 1997.
[32] Johann Heinrich Lambert. Observations variae in mathesin puram. Acta Helveticae physico-mathematico-anatomico-botanico-medica, Band III, pages 128-168, 1758.