Asia
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo
In this paper we are concerned with investigating theperformance ofNN-based methods foranomaly detection. We firstshowthrough extensivesimulations thatNNmethods compare favorably to some of the other state-of-the-art algorithms for anomaly detection based on a setofbenchmark syntheticdatasets.
Robust Learning of Fixed-Structure Bayesian Networks
Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart
Neural Information Processing Systems http://nips.cc/
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
Adversarial training [10, 16, 18], which injects adversarially perturbed dataintotraining data,isapromising approach. Many other heuristics have been developed to make neural networks insensitive against small perturbations on inputs.
Unified Sample-Optimal Property Estimation in Near-Linear Time
Let k be the collection of distributions over the alphabet[k]:= {1,2,...,k}, and [k] be the set of finite sequences over[k].
A convex program for bilinear inversion of sparse vectors
Alireza Aghasi, Ali Ahmed, Paul Hand, Babhru Joshi
Neural Information Processing Systems http://nips.cc/
