Statistical Learning
A Smoother Way to Train Structured Prediction Models
Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaid Harchaoui
We present a framework to train a structured prediction model by performing smoothing ontheinference algorithm itbuilds upon.
Stochastic Nonparametric Event-Tensor Decomposition
Tensor decompositions are fundamental tools for multiway data analysis.
Structured Graph Learning Via Laplacian Spectral Constraints
Sandeep Kumar, Jiaxi Ying, Jose Vinicius de Miranda Cardoso, Daniel Palomar
Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. It is well known that structured graph learning from observedsamples isanNP-hard combinatorial problem. In this paper, we first show that for a set of important graph families it is possible toconvertthestructural constraints ofstructure intoeigenvalueconstraints ofthe graph Laplacianmatrix.
Poisson Variational Autoencoder
Despite their success, traditional V AEs rely on continuous latent variables, which deviates sharply from the discrete nature of biological neurons.
Markov Random Fields for Collaborative Filtering
In this regime (n > m), learning a sparse model may not lead to significant improvements inprediction accuracy(compared toadensemodel).
