In statistical relational learning, the link prediction problem is key to automatically
understand the structure of large knowledge bases. As in previous studies, we propose to …

In statistical relational learning, knowledge graph completion deals with automatically
understanding the structure of large knowledge graphs--labeled directed graphs--and …

Motivated by recent revelations of widespread state surveillance of personal communication,
many solutions now claim to offer secure and private messaging. This includes both a large …

Extremal Finite Set Theory surveys old and new results in the area of extremal set system
theory. It presents an overview of the main techniques and tools (shifting, the cycle method …

Practical and pervasive needs for robustness and privacy in algorithms have inspired the
design of online adversarial and differentially private learning algorithms. The primary …

We study bilinear embedding models for the task of multi-relational link prediction and
knowledge graph completion. Bilinear models belong to the most basic models for this task …

Low-dimensional embeddings, from classical spectral embeddings to modern neural-net-
inspired methods, are a cornerstone in the modeling and analysis of complex networks …

Statistical query (SQ) algorithms are algorithms that have access to an\em SQ oracle for the
input distribution $ D $ instead of iid samples from $ D $. Given a query function $ φ: X\to [-1 …

This thesis introduces quantum natural language processing (QNLP) models based on a
simple yet powerful analogy between computational linguistics and quantum mechanics …

We present and study approximate notions of dimensional and margin complexity, which
correspond to the minimal dimension or norm of an embedding required to {\em …