14,997 research outputs found
A Practical Guide to Robust Optimization
Robust optimization is a young and active research field that has been mainly
developed in the last 15 years. Robust optimization is very useful for
practice, since it is tailored to the information at hand, and it leads to
computationally tractable formulations. It is therefore remarkable that
real-life applications of robust optimization are still lagging behind; there
is much more potential for real-life applications than has been exploited
hitherto. The aim of this paper is to help practitioners to understand robust
optimization and to successfully apply it in practice. We provide a brief
introduction to robust optimization, and also describe important do's and
don'ts for using it in practice. We use many small examples to illustrate our
discussions
Deep controlled learning of dynamic policies with an application to lost-sales inventory control
Recent literature established that neural networks can represent good
policies across a range of stochastic dynamic models in supply chain and
logistics. We propose a new algorithm that incorporates variance reduction
techniques, to overcome limitations of algorithms typically employed in
literature to learn such neural network policies. For the classical lost sales
inventory model, the algorithm learns neural network policies that are vastly
superior to those learned using model-free algorithms, while outperforming the
best heuristic benchmarks by an order of magnitude. The algorithm is an
interesting candidate to apply to other stochastic dynamic problems in supply
chain and logistics, because the ideas in its development are generic
Global supply chains of high value low volume products
Imperial Users onl
Breaking Sticks and Ambiguities with Adaptive Skip-gram
Recently proposed Skip-gram model is a powerful method for learning
high-dimensional word representations that capture rich semantic relationships
between words. However, Skip-gram as well as most prior work on learning word
representations does not take into account word ambiguity and maintain only
single representation per word. Although a number of Skip-gram modifications
were proposed to overcome this limitation and learn multi-prototype word
representations, they either require a known number of word meanings or learn
them using greedy heuristic approaches. In this paper we propose the Adaptive
Skip-gram model which is a nonparametric Bayesian extension of Skip-gram
capable to automatically learn the required number of representations for all
words at desired semantic resolution. We derive efficient online variational
learning algorithm for the model and empirically demonstrate its efficiency on
word-sense induction task
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