The Living Thing / Notebooks :

Adversarial learning

Statistics against Shayṭtān

General adversarial, as opposed to stochastic, learning.

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.

Refs

AABR09
Abernethy, J., Agarwal, A., Bartlett, P. L., & Rakhlin, A. (2009) A Stochastic View of Optimal Regret through Minimax Duality. ArXiv:0903.5328 [Cs, Stat].
ArBo17
Arjovsky, M., & Bottou, L. (2017) Towards Principled Methods for Training Generative Adversarial Networks. ArXiv:1701.04862 [Stat].
ArCB17
Arjovsky, M., Chintala, S., & Bottou, L. (2017) Wasserstein GAN. ArXiv:1701.07875 [Cs, Stat].
AGLM17
Arora, S., Ge, R., Liang, Y., Ma, T., & Zhang, Y. (2017) Generalization and Equilibrium in Generative Adversarial Nets (GANs). ArXiv:1703.00573 [Cs].
BJPD17
Bora, A., Jalal, A., Price, E., & Dimakis, A. G.(2017) Compressed Sensing using Generative Models. ArXiv:1703.03208 [Cs, Math, Stat].
BuCe12
Bubeck, S., & Cesa-Bianchi, N. (2012) Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends® in Machine Learning, 5(1), 1–122. DOI.
BuSl12
Bubeck, S., & Slivkins, A. (2012) The best of both worlds: stochastic and adversarial bandits. ArXiv:1202.4473 [Cs].
GKNT17
Ghosh, A., Kulharia, V., Namboodiri, V., Torr, P. H. S., & Dokania, P. K.(2017) Multi-Agent Diverse Generative Adversarial Networks. ArXiv:1704.02906 [Cs, Stat].
GPMX14
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014) Generative Adversarial Networks. ArXiv:1406.2661 [Cs, Stat].
GoSS14
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) Explaining and Harnessing Adversarial Examples. ArXiv:1412.6572 [Cs, Stat].
GHLY17
Guo, X., Hong, J., Lin, T., & Yang, N. (2017) Relaxed Wasserstein with Applications to GANs. ArXiv:1705.07164 [Cs, Stat].
JeBV16
Jetchev, N., Bergmann, U., & Vollgraf, R. (2016) Texture Synthesis with Spatial Generative Adversarial Networks. In Advances in Neural Information Processing Systems 29.
KhJL16
Khim, J., Jog, V., & Loh, P.-L. (2016) Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis. ArXiv:1611.00350 [Cs, Stat].
LSLW15
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2015) Autoencoding beyond pixels using a learned similarity metric. ArXiv:1512.09300 [Cs, Stat].
PASA16
Poole, B., Alemi, A. A., Sohl-Dickstein, J., & Angelova, A. (2016) Improved generator objectives for GANs. In Advances in Neural Information Processing Systems 29.
RaMC15
Radford, A., Metz, L., & Chintala, S. (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. ArXiv:1511.06434 [Cs].