69 research outputs found
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
In the predict-then-optimize framework, the objective is to train a
predictive model, mapping from environment features to parameters of an
optimization problem, which maximizes decision quality when the optimization is
subsequently solved. Recent work on decision-focused learning shows that
embedding the optimization problem in the training pipeline can improve
decision quality and help generalize better to unseen tasks compared to relying
on an intermediate loss function for evaluating prediction quality. We study
the predict-then-optimize framework in the context of sequential decision
problems (formulated as MDPs) that are solved via reinforcement learning. In
particular, we are given environment features and a set of trajectories from
training MDPs, which we use to train a predictive model that generalizes to
unseen test MDPs without trajectories. Two significant computational challenges
arise in applying decision-focused learning to MDPs: (i) large state and action
spaces make it infeasible for existing techniques to differentiate through MDP
problems, and (ii) the high-dimensional policy space, as parameterized by a
neural network, makes differentiating through a policy expensive. We resolve
the first challenge by sampling provably unbiased derivatives to approximate
and differentiate through optimality conditions, and the second challenge by
using a low-rank approximation to the high-dimensional sample-based
derivatives. We implement both Bellman--based and policy gradient--based
decision-focused learning on three different MDP problems with missing
parameters, and show that decision-focused learning performs better in
generalization to unseen tasks
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Decision-focused learning (DFL) is an emerging paradigm in machine learning
which trains a model to optimize decisions, integrating prediction and
optimization in an end-to-end system. This paradigm holds the promise to
revolutionize decision-making in many real-world applications which operate
under uncertainty, where the estimation of unknown parameters within these
decision models often becomes a substantial roadblock. This paper presents a
comprehensive review of DFL. It provides an in-depth analysis of the various
techniques devised to integrate machine learning and optimization models,
introduces a taxonomy of DFL methods distinguished by their unique
characteristics, and conducts an extensive empirical evaluation of these
methods proposing suitable benchmark dataset and tasks for DFL. Finally, the
study provides valuable insights into current and potential future avenues in
DFL research.Comment: Experimental Survey and Benchmarkin
Fairguard: Harness Logic-based Fairness Rules in Smart Cities
Smart cities operate on computational predictive frameworks that collect,
aggregate, and utilize data from large-scale sensor networks. However, these
frameworks are prone to multiple sources of data and algorithmic bias, which
often lead to unfair prediction results. In this work, we first demonstrate
that bias persists at a micro-level both temporally and spatially by studying
real city data from Chattanooga, TN. To alleviate the issue of such bias, we
introduce Fairguard, a micro-level temporal logic-based approach for fair smart
city policy adjustment and generation in complex temporal-spatial domains. The
Fairguard framework consists of two phases: first, we develop a static
generator that is able to reduce data bias based on temporal logic conditions
by minimizing correlations between selected attributes. Then, to ensure
fairness in predictive algorithms, we design a dynamic component to regulate
prediction results and generate future fair predictions by harnessing logic
rules. Evaluations show that logic-enabled static Fairguard can effectively
reduce the biased correlations while dynamic Fairguard can guarantee fairness
on protected groups at run-time with minimal impact on overall performance.Comment: This paper was accepted by the 8th ACM/IEEE Conference on Internet of
Things Design and Implementatio
A bilevel framework for decision-making under uncertainty with contextual information
In this paper, we propose a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Given a finite collection of observations of the uncertain parameters and potential explanatory variables (i.e., the contextual information), our approach fits a parametric model to those data that is specifically tailored to maximizing the decision value, while accounting for possible feasibility constraints. From a mathematical point of view, our framework translates into a bilevel program, for which we provide both a fast regularization procedure and a big-M-based reformulation that can be solved using off-the-shelf optimization solvers. We showcase the benefits of moving from the traditional scheme for model estimation (based on statistical quality metrics) to decision-guided prediction using three different practical problems. We also compare our approach with existing ones in a realistic case study that considers a strategic power producer that participates in the Iberian electricity market. Finally, we use these numerical simulations to analyze the conditions (in terms of the firmâs cost structure and production capacity) under which our approach proves to be more advantageous to the producer.This work was supported in part by the European Research Council (ERC) under the EU Horizon 2020 research and innovation program (grant agreement No. 755705), in part by the Spanish Ministry of Science and Innovation (AEI/10.13039/501100011033) through project PID2020-115460GB-I00, and in part by the Junta de AndalucĂa (JA), the Universidad de MĂĄlaga and the European Regional Development Fund (FEDER) through the research projects P20_00153 and UMA2018âFEDERJAâ001. M. Ă. Muñoz is also funded by the Spanish Ministry of Science, Innovation and Universities through the State Training Subprogram 2018 of the State Program for the Promotion of Talent and its Employability in R&D&I, within the framework of the State Plan for Scientific and Technical Research and Innovation 2017-2020 and by the European Social Fund. Finally, the authors thankfully acknowledge the computer resources, technical expertise, and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Malaga
Feature-driven improvement of renewable energy forecasting and trading
M. A. Muñoz, J. M. Morales, and S. Pineda, Feature-driven Improvement of Renewable Energy Forecasting and Trading, IEEE Transactions on Power Systems, 2020.Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy forecasts and the competitive edge of renewable energy producers in electricity markets with a dual-price settlement of imbalances. The quality and economic gains brought by the proposed procedure essentially stem from the utilization of valuable predictors (also known as features) in a data-driven newsvendor model that renders a computationally inexpensive linear program. We illustrate the proposed procedure and numerically assess its benefits on a realistic case study that considers the aggregate wind power production in the Danish DK1 bidding zone as the variable to be predicted and traded. Within this context, our procedure leverages, among others, spatial information in the form of wind power forecasts issued by transmission system operators (TSO) in surrounding bidding zones and publicly available in online platforms. We show that our method is able to improve the quality of the wind power forecast issued by the Danish TSO by several percentage points (when measured in terms of the mean absolute or the root mean square error) and to significantly reduce the balancing costs incurred by the wind power producer.European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (grant agreement No. 755705)
Spanish Ministry of Economy, Industry, and Competitiveness through project ENE2017-83775-P
The intersection of machine learning with forecasting and optimisation: theory and applications
Forecasting and optimisation are two major fields of operations research that
are widely used in practice. These methods have contributed to each other
growth in several ways. However, the nature of the relationship between these
two fields and integrating them have not been explored or understood enough. We
advocate the integration of these two fields and explore several problems that
require both forecasting and optimisation to deal with the uncertainties. We
further investigate some of the methodologies that lie at the intersection of
machine learning with prediction and optimisation to address real-world
problems. Finally, we provide several research directions for those interested
to work in this domain
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