Statistical Learning
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami
Neural Information Processing Systems http://nips.cc/
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Neural Information Processing Systems http://nips.cc/
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
Neural Information Processing Systems http://nips.cc/
Observational-Interventional Priors for Dose-Response Learning
Neural Information Processing Systems http://nips.cc/
Adversarial Multiclass Classification: A Risk Minimization Perspective
Rizal Fathony, Anqi Liu, Kaiser Asif, Brian Ziebart
Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses. In contrast with empirical risk minimization (ERM) methods, which use convex surrogate losses to approximate the desired non-convex target loss function, adversarial methods minimize non-convex losses by treating the properties of the training data as being uncertain and worst case within a minimax game. Despite this difference in formulation, we recast adversarial classification under zero-one loss as an ERM method with a novel prescribed loss function. We demonstrate a number of theoretical and practical advantages over the very closely related hinge loss ERM methods. This establishes adversarial classification under the zero-one loss as a method that fills the long standing gap in multiclass hinge loss classification, simultaneously guaranteeing Fisher consistency and universal consistency, while also providing dual parameter sparsity and high accuracy predictions in practice.
Efficient Nonparametric Smoothness Estimation
Shashank Singh, Simon S. Du, Barnabas Poczos
Neural Information Processing Systems http://nips.cc/
Stochastic Gradient MCMC with Stale Gradients
Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin
Neural Information Processing Systems http://nips.cc/
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
Sanghamitra Dutta, Viveck Cadambe, Pulkit Grover
Neural Information Processing Systems http://nips.cc/
Statistical Inference for Cluster Trees
Jisu KIM, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman
A cluster tree provides a highly-interpretable summary of a density function by representing the hierarchy of its high-density clusters. It is estimated using the empirical tree, which is the cluster tree constructed from a density estimator. This paper addresses the basic question of quantifying our uncertainty by assessing the statistical significance of topological features of an empirical cluster tree. We first study a variety of metrics that can be used to compare different trees, analyze their properties and assess their suitability for inference. We then propose methods to construct and summarize confidence sets for the unknown true cluster tree. We introduce a partial ordering on cluster trees which we use to prune some of the statistically insignificant features of the empirical tree, yielding interpretable and parsimonious cluster trees. Finally, we illustrate the proposed methods on a variety of synthetic examples and furthermore demonstrate their utility in the analysis of a Graft-versus-Host Disease (GvHD) data set.