We analyze stochastic gradient descent for optimizing non-convex functions. In many cases
for non-convex functions the goal is to find a reasonable local minimum, and the main …

Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

Each day humans generate massive volumes of data in a variety of different forms (Lazer et
al., 2009). For example, digitized texts provide a rich source of political content through …

Abstract Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in
statistical learning of latent variable models and in data mining. In this paper, we propose …

Community detection is the task of detecting hidden communities from observed
interactions. Guaranteed community detection has so far been mostly limited to models with …

In this paper, we provide local and global convergence guarantees for recovering CP
(Candecomp/Parafac) tensor decomposition. The main step of the proposed algorithm is a …

Modeling community formation and detecting hidden communities in networks is a well
studied problem. However, theoretical analysis of community detection has been mostly …

An important objective for analyzing real-world graphs is to achieve scalable performance
on large, streaming graphs. A challenging and relevant example is the graph partition …

Tensor factorization arises in many machine learning applications, such as knowledge base
modeling and parameter estimation in latent variable models. However, numerical methods …