General adversarial learning, where the noise is not purely random, but chosen to be the worst possible noise for you.
As renewed in fame recently by generative adversarial networks.
TBC: discuss politics implied by treating the learning as a battle with a conniving adversary as opposed to an uncaring universe, mention obvious connection with the theist neoreactionary zeitgeist. I'm sure someone has done this well in a terribly eloquent blog post, but I haven't found one I'd want to link to yet.
Regardless of politically suggestive structure, application of game theory in the place of pure randomness and probably intersting / non-controversial in many areas although I don't know most of them. Adverarial bandits is the obvious one in my world.
- AABR09: Jacob Abernethy, Alekh Agarwal, Peter L Bartlett, Alexander Rakhlin (2009) A Stochastic View of Optimal Regret through Minimax Duality. ArXiv:0903.5328 [Cs, Stat].
- LSLW15: Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther (2015) Autoencoding beyond pixels using a learned similarity metric. ArXiv:1512.09300 [Cs, Stat].
- BJPD17: Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis (2017) Compressed Sensing using Generative Models. In International Conference on Machine Learning (pp. 537–546).
- KhJL16: Justin Khim, Varun Jog, Po-Ling Loh (2016) Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis. ArXiv:1611.00350 [Cs, Stat].
- GoSS14: Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy (2014) Explaining and Harnessing Adversarial Examples. ArXiv:1412.6572 [Cs, Stat].
- ZhZh17: Rui Zhang, Quanyan Zhu (2017) Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries. ArXiv:1710.04677 [Cs, Stat].
- AGLM17: Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang (2017) Generalization and Equilibrium in Generative Adversarial Nets (GANs). ArXiv:1703.00573 [Cs].
- GPMX14: Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, … Yoshua Bengio (2014) Generative Adversarial Networks. ArXiv:1406.2661 [Cs, Stat].
- PASA16: Ben Poole, Alexander A. Alemi, Jascha Sohl-Dickstein, Anelia Angelova (2016) Improved generator objectives for GANs. In Advances in Neural Information Processing Systems 29.
- GKNT17: Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H. S. Torr, Puneet K. Dokania (2017) Multi-Agent Diverse Generative Adversarial Networks. ArXiv:1704.02906 [Cs, Stat].
- BuCe12: Sébastien Bubeck, Nicolò Cesa-Bianchi (2012) Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends® in Machine Learning, 5(1), 1–122. DOI
- GHLY17: Xin Guo, Johnny Hong, Tianyi Lin, Nan Yang (2017) Relaxed Wasserstein with Applications to GANs. ArXiv:1705.07164 [Cs, Stat].
- JeBV16: Nikolay Jetchev, Urs Bergmann, Roland Vollgraf (2016) Texture Synthesis with Spatial Generative Adversarial Networks. In Advances in Neural Information Processing Systems 29.
- BuSl12: Sebastien Bubeck, Aleksandrs Slivkins (2012) The best of both worlds: stochastic and adversarial bandits. ArXiv:1202.4473 [Cs].
- ArBo17: Martin Arjovsky, Léon Bottou (2017) Towards Principled Methods for Training Generative Adversarial Networks. ArXiv:1701.04862 [Stat].
- RaMC15: Alec Radford, Luke Metz, Soumith Chintala (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In arXiv:1511.06434 [cs].
- ArCB17: Martin Arjovsky, Soumith Chintala, Léon Bottou (2017) Wasserstein Generative Adversarial Networks. In International Conference on Machine Learning (pp. 214–223).