@conference {1499817,
	title = {Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization },
	booktitle = {AAAI conference on Artificial Intelligence (AAAI-19)},
	year = {2019},
	abstract = {Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to
predictive models, to decisions. These components are typically approached separately: a machine learning model is
first trained via a measure of predictive accuracy, and then its
predictions are used as input into an optimization algorithm
which produces a decision. However, the loss function used to
train the model may easily be misaligned with the end goal,
which is to make the best decisions possible. Hand-tuning
the loss function to align with optimization is a difficult and
error-prone process (which is often skipped entirely).
We focus on combinatorial optimization problems and introduce a general framework for decision-focused learning,
where the machine learning model is directly trained in conjunction with the optimization algorithm to produce highquality decisions. Technically, our contribution is a means
of integrating common classes of discrete optimization problems into deep learning or other predictive models, which are
typically trained via gradient descent. The main idea is to use
a continuous relaxation of the discrete problem to propagate
gradients through the optimization procedure. We instantiate
this framework for two broad classes of combinatorial problems: linear programs and submodular maximization. Experimental results across a variety of domains show that decisionfocused learning often leads to improved optimization performance compared to traditional methods. We find that standard
measures of accuracy are not a reliable proxy for a predictive
model{\textquoteright}s utility in optimization, and our method{\textquoteright}s ability to
specify the true goal as the model{\textquoteright}s training objective yields
substantial dividends across a range of decision problems.},
	author = {Bryan Wilder and Dilkina, Bistra and Tambe, Milind}
}
