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 …

The secretary and the prophet inequality problems are central to the field of Stop**
Theory. Recently, there has been a lot of work in generalizing these models to multiple items …

Many tasks in machine learning and data mining, such as data diversification, non-
parametric learning, kernel machines, clustering etc., require extracting a small but …

An effective technique for solving optimization problems over massive data sets is to
partition the data into smaller pieces, solve the problem on each piece and compute a …

Online bipartite matching is one of the most fundamental problems in the online algorithms
literature. Karp, Vazirani, and Vazirani (STOC 1990) gave an elegant algorithm for …

We introduce a novel framework of Prophet Inequalities for combinatorial valuation
functions. For a (n on-monotone) submodular objective function over an arbitrary matroid …

In correlation clustering we are given a set of points along with recommendations whether
each pair of points should be placed in the same cluster or into separate clusters. The goal …

Nearly thirty years ago, Bar-Noy, Motwani and Naor [IPL'92] conjectured that an online (1+ o
(1)) Δ-edge-coloring algorithm exists for n-node graphs of maximum degree Δ= ω (log n) …

We study streaming submodular maximization subject to matching/b-matching constraints
(MSM/MSbM), and present improved upper and lower bounds for these problems. On the …

In this paper we consider the following question: can we optimize objective functions from
the training data we use to learn them? We formalize this question through a novel …