63 research outputs found
Bootstrap Robust Prescriptive Analytics
We address the problem of prescribing an optimal decision in a framework
where its cost depends on uncertain problem parameters that need to be
learned from data. Earlier work by Bertsimas and Kallus (2014) transforms
classical machine learning methods that merely predict from supervised
training data into prescriptive methods
taking optimal decisions specific to a particular covariate context .
Their prescriptive methods factor in additional observed contextual information
on a potentially large number of covariates to take context specific
actions which are superior to any static decision . Any naive
use of limited training data may, however, lead to gullible decisions
over-calibrated to one particular data set. In this paper, we borrow ideas from
distributionally robust optimization and the statistical bootstrap of Efron
(1982) to propose two novel prescriptive methods based on (nw) Nadaraya-Watson
and (nn) nearest-neighbors learning which safeguard against overfitting and
lead to improved out-of-sample performance. Both resulting robust prescriptive
methods reduce to tractable convex optimization problems and enjoy a limited
disappointment on bootstrap data. We illustrate the data-driven decision-making
framework and our novel robustness notion on a small news vendor problem as
well as a small portfolio allocation problem
Optimal Transport in the Face of Noisy Data
Optimal transport distances are popular and theoretically well understood in
the context of data-driven prediction. A flurry of recent work has popularized
these distances for data-driven decision-making as well although their merits
in this context are far less well understood. This in contrast to the more
classical entropic distances which are known to enjoy optimal statistical
properties. This begs the question when, if ever, optimal transport distances
enjoy similar statistical guarantees. Optimal transport methods are shown here
to enjoy optimal statistical guarantees for decision problems faced with noisy
data
Holistic Robust Data-Driven Decisions
The design of data-driven formulations for machine learning and
decision-making with good out-of-sample performance is a key challenge. The
observation that good in-sample performance does not guarantee good
out-of-sample performance is generally known as overfitting. Practical
overfitting can typically not be attributed to a single cause but instead is
caused by several factors all at once. We consider here three overfitting
sources: (i) statistical error as a result of working with finite sample data,
(ii) data noise which occurs when the data points are measured only with finite
precision, and finally (iii) data misspecification in which a small fraction of
all data may be wholly corrupted. We argue that although existing data-driven
formulations may be robust against one of these three sources in isolation they
do not provide holistic protection against all overfitting sources
simultaneously. We design a novel data-driven formulation which does guarantee
such holistic protection and is furthermore computationally viable. Our
distributionally robust optimization formulation can be interpreted as a novel
combination of a Kullback-Leibler and Levy-Prokhorov robust optimization
formulation which is novel in its own right. However, we show how in the
context of classification and regression problems that several popular
regularized and robust formulations reduce to a particular case of our proposed
novel formulation. Finally, we apply the proposed HR formulation on a portfolio
selection problem with real stock data, and analyze its risk/return tradeoff
against several benchmarks formulations. Our experiments show that our novel
ambiguity set provides a significantly better risk/return trade-off
Optimal Learning for Structured Bandits
We study structured multi-armed bandits, which is the problem of online
decision-making under uncertainty in the presence of structural information. In
this problem, the decision-maker needs to discover the best course of action
despite observing only uncertain rewards over time. The decision-maker is aware
of certain structural information regarding the reward distributions and would
like to minimize their regret by exploiting this information, where the regret
is its performance difference against a benchmark policy that knows the best
action ahead of time. In the absence of structural information, the classical
upper confidence bound (UCB) and Thomson sampling algorithms are well known to
suffer only minimal regret. As recently pointed out, neither algorithms are,
however, capable of exploiting structural information that is commonly
available in practice. We propose a novel learning algorithm that we call DUSA
whose worst-case regret matches the information-theoretic regret lower bound up
to a constant factor and can handle a wide range of structural information. Our
algorithm DUSA solves a dual counterpart of the regret lower bound at the
empirical reward distribution and follows its suggested play. Our proposed
algorithm is the first computationally viable learning policy for structured
bandit problems that has asymptotic minimal regret
Learning for Robust Optimization
We propose a data-driven technique to automatically learn the uncertainty
sets in robust optimization. Our method reshapes the uncertainty sets by
minimizing the expected performance across a family of problems while
guaranteeing constraint satisfaction. We learn the uncertainty sets using a
novel stochastic augmented Lagrangian method that relies on differentiating the
solutions of the robust optimization problems with respect to the parameters of
the uncertainty set. We show sublinear convergence to stationary points under
mild assumptions, and finite-sample probabilistic guarantees of constraint
satisfaction using empirical process theory. Our approach is very flexible and
can learn a wide variety of uncertainty sets while preserving tractability.
Numerical experiments show that our method outperforms traditional approaches
in robust and distributionally robust optimization in terms of out of sample
performance and constraint satisfaction guarantees. We implemented our method
in the open-source package LROPT
Mean Robust Optimization
Robust optimization is a tractable and expressive technique for
decision-making under uncertainty, but it can lead to overly conservative
decisions when pessimistic assumptions are made on the uncertain parameters.
Wasserstein distributionally robust optimization can reduce conservatism by
being data-driven, but it often leads to very large problems with prohibitive
solution times. We introduce mean robust optimization, a general framework that
combines the best of both worlds by providing a trade-off between computational
effort and conservatism. We propose uncertainty sets constructed based on
clustered data rather than on observed data points directly thereby
significantly reducing problem size. By varying the number of clusters, our
method bridges between robust and Wasserstein distributionally robust
optimization. We show finite-sample performance guarantees and explicitly
control the potential additional pessimism introduced by any clustering
procedure. In addition, we prove conditions for which, when the uncertainty
enters linearly in the constraints, clustering does not affect the optimal
solution. We illustrate the efficiency and performance preservation of our
method on several numerical examples, obtaining multiple orders of magnitude
speedups in solution time with little-to-no effect on the solution quality
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