Statistical Learning
Kernel functions based on triplet comparisons
Matthäus Kleindessner, Ulrike von Luxburg
Given only information in the form of similarity triplets "Object A is more similar
Minimax Estimation of Bandable Precision Matrices
Sparse edge recovery is generally not easy for an arbitrary distribution.
Bayesian Intermittent Demand Forecasting for Large Inventories
Matthias W. Seeger, David Salinas, Valentin Flunkert
There, demand is highly intermittent and bursty: long runs of zeros, with islands of high counts.
Generalized Linear Model Regression under Distance-to-set Penalties
Jason Xu, Eric Chi, Kenneth Lange
Neural Information Processing Systems http://nips.cc/
FALKON: An Optimal Large Scale Kernel Method
Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco
Neural Information Processing Systems http://nips.cc/
Non-parametric Structured Output Networks
Deep neural networks (DNNs) and probabilistic graphical models (PGMs) are the two main tools for statistical modeling.
Query Complexity of Clustering with Side Information
Our algorithms are all efficient, and parameter free, i.e., they work without
Learning Multiple Tasks with Multilinear Relationship Networks
Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Philip S. Yu
Unfortunately, if cross-task knowledge transfer is impossible, then we will overfit each task due to limited amount of labeled data. One way to circumvent this dilemma is to use an external data source, e.g.
Model-Powered Conditional Independence Test
We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables. Given i.i.d samples from the joint distribution