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Abstract Information-theoretic quantities like entropy and mutual information have found
numerous uses in machine learning. It is well known that there is a strong connection …

Information-theoretic quantities like entropy and mutual information have found numerous
uses in machine learning. It is well known that there is a strong connection between these …

Finding a\emph {single} best solution is the most common objective in combinatorial
optimization problems. However, such a single solution may not be applicable to real-world …

We start with an overview of a class of submodular functions called SCMMs (sums of
concave composed with non-negative modular functions plus a final arbitrary modular). We …

Abstract\emph {Finding diverse solutions} to combinatorial optimization problems is
beneficial for a deeper understanding of complicated real-world problems and for simpler …

Learning an appropriate distance metric is a crucial problem in pattern recognition. To
confront with the scalability issue of massive data, hamming distance on binary codes is …

A good clustering can help a data analyst to explore and understand a data set, but what
constitutes a good clustering may depend on domain-specific and application-specific …

Empirical risk minimization frequently employs convex surrogates to underlying discrete loss
functions in order to achieve computational tractability during optimization. However …

This paper presents group feedback protocols (group NACK (gNACK)/group ACK (gACK)),
whereby a swarm of Unmanned Aerial Vehicles (UAV) cooperate through their Sidelinks …