The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

Many real-world analytics problems involve two significant challenges: prediction and
optimization. Because of the typically complex nature of each challenge, the standard …

It is almost always easier to find an accurate-but-complex model than an accurate-yet-simple
model. Finding optimal, sparse, accurate models of various forms (linear models with integer …

Purpose is to develop a new approach to the design of mining operations basing upon
models and methods of decision making. Methods. The paper has applied a complex …

The purpose of the work is to develop new approaches to finding economic and
environmental strategies for field development. The complex method is applied in the work …

This paper introduces a model-based reinforcement learning (MBRL) framework that
incorporates the underlying decision problem in learning the transition model of the …

Unconditional cash transfers to the extreme poor via mobile telephony represent a radical,
new approach to giving. GiveDirectly is a non-governmental organization (NGO) at the …

This work proposes a way to align statistical modeling with decision making. We provide a
method that propagates the uncertainty in predictive modeling to the uncertainty in …

We consider stochastic optimization with side information where, prior to decision-making,
covariate data are available to inform better decisions. To hedge against data uncertainty …