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From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
Krzysztof M. Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani
Neural Information Processing Systems http://nips.cc/
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
Ali Kavis, Kfir Y. Levy, Francis Bach, Volkan Cevher
Stochastic constrained optimization with first-order oracles (SCO) is critical in machine learning.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Stanislav Pidhorskyi, Ranya Almohsen, Gianfranco Doretto
We assume that training data is available to describe only the inlier distribution. Recent approaches primarily leverage deep encoder-decoder network architectures to compute a reconstruction error that is used to either compute a novelty score or to train a one-class classifier.
A probabilistic population code based on neural samples
Sabyasachi Shivkumar, Richard Lange, Ankani Chattoraj, Ralf Haefner
Neural Information Processing Systems http://nips.cc/
Accurate, reliable and fast robustness evaluation
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
Neural Information Processing Systems http://nips.cc/
Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm
However, it is insufficient to evaluate the stochastic algorithm not to consider the generalization performance, which is roughly the gap between Eq.