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Found 7 papers, 0 papers with code
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data.
A Unified Framework for Stochastic Matrix Factorization via Variance Reduction
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction.
Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies
We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm.
A Unified Convergence Analysis of the Multiplicative Update Algorithm for Regularized Nonnegative Matrix Factorization
The multiplicative update (MU) algorithm has been extensively used to estimate the basis and coefficient matrices in nonnegative matrix factorization (NMF) problems under a wide range of divergences and regularizers.
Online Nonnegative Matrix Factorization with General Divergences
We develop a unified and systematic framework for performing online nonnegative matrix factorization under a wide variety of important divergences.
Online Nonnegative Matrix Factorization with Outliers
We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers.
Adversarial Top-$K$ Ranking
We study the top-$K$ ranking problem where the goal is to recover the set of top-$K$ ranked items out of a large collection of items based on partially revealed preferences.
