Log-Distributional Approach for Learning Covariate Shift Ratios

Distributional Reinforcement Learning theory suggests that distributional fixed points could play a fundamental role to learning non additive value functions. In particular, we propose a distributional approach for learning Covariate Shift Ratios, whose update rule is originally multiplicative

Tractable multi-product pricing under discrete choice models

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 199-204).We consider a retailer offering an assortment of differentiated substitutable products to price-sensitive customers. Prices are chosen to maximize profit, subject to inventory/ capacity constraints, as well as more general constraints. The profit is not even a quasi-concave function of the prices under the basic multinomial logit (MNL) demand model. Linear constraints can induce a non-convex feasible region. Nevertheless, we show how to efficiently solve the pricing problem under three important, more general families of demand models. Generalized attraction (GA) models broaden the range of nonlinear responses to changes in price. We propose a reformulation of the pricing problem over demands (instead of prices) which is convex. We show that the constrained problem under MNL models can be solved in a polynomial number of Newton iterations. In experiments, our reformulation is solved in seconds rather than days by commercial software. For nested-logit (NL) demand models, we show that the profit is concave in the demands (market shares) when all the price-sensitivity parameters are sufficiently close. The closed-form expressions for the Hessian of the profit that we derive can be used with general-purpose nonlinear solvers. For the special (unconstrained) case already considered in the literature, we devise an algorithm that requires no assumptions on the problem parameters. The class of generalized extreme value (GEV) models includes the NL as well as the cross-nested logit (CNL) model. There is generally no closed form expression for the profit in terms of the demands. We nevertheless how the gradient and Hessian can be computed for use with general-purpose solvers. We show that the objective of a transformed problem is nearly concave when all the price sensitivities are close. For the unconstrained case, we develop a simple and surprisingly efficient first-order method. Our experiments suggest that it always finds a global optimum, for any model parameters. We apply the method to mixed logit (MMNL) models, by showing that they can be approximated with CNL models. With an appropriate sequence of parameter scalings, we conjecture that the solution found is also globally optimal.by Philipp Wilhelm Keller.Ph.D

Scheduling Virtual Conferences Fairly: {A}chieving Equitable Participant and Speaker Satisfaction

Recently, almost all conferences have moved to virtual mode due to the pandemic-induced restrictions on travel and social gathering. Contrary to in-person conferences, virtual conferences face the challenge of efficiently scheduling talks, accounting for the availability of participants from different timezones and their interests in attending different talks. A natural objective for conference organizers is to maximize efficiency, e.g., total expected audience participation across all talks. However, we show that optimizing for efficiency alone can result in an unfair virtual conference schedule, where individual utilities for participants and speakers can be highly unequal. To address this, we formally define fairness notions for participants and speakers, and derive suitable objectives to account for them. As the efficiency and fairness objectives can be in conflict with each other, we propose a joint optimization framework that allows conference organizers to design schedules that balance (i.e., allow trade-offs) among efficiency, participant fairness and speaker fairness objectives. While the optimization problem can be solved using integer programming to schedule smaller conferences, we provide two scalable techniques to cater to bigger conferences. Extensive evaluations over multiple real-world datasets show the efficacy and flexibility of our proposed approaches.Comment: In proceedings of the Thirty-first Web Conference (WWW-2022). arXiv admin note: text overlap with arXiv:2010.1462

Distributed Constraint Optimization:Privacy Guarantees and Stochastic Uncertainty

Distributed Constraint Satisfaction (DisCSP) and Distributed Constraint Optimization (DCOP) are formal frameworks that can be used to model a variety of problems in which multiple decision-makers cooperate towards a common goal: from computing an equilibrium of a game, to vehicle routing problems, to combinatorial auctions. In this thesis, we independently address two important issues in such multi-agent problems: 1) how to provide strong guarantees on the protection of the privacy of the participants, and 2) how to anticipate future, uncontrollable events. On the privacy front, our contributions depart from previous work in two ways. First, we consider not only constraint privacy (the agents' private costs) and decision privacy (keeping the complete solution secret), but also two other types of privacy that have been largely overlooked in the literature: agent privacy, which has to do with protecting the identities of the participants, and topology privacy, which covers information about the agents' co-dependencies. Second, while previous work focused mainly on quantitatively measuring and reducing privacy loss, our algorithms provide stronger, qualitative guarantees on what information will remain secret. Our experiments show that it is possible to provide such privacy guarantees, while still scaling to much larger problems than the previous state of the art. When it comes to reasoning under uncertainty, we propose an extension to the DCOP framework, called DCOP under Stochastic Uncertainty (StochDCOP), which includes uncontrollable, random variables with known probability distributions that model uncertain, future events. The problem becomes one of making "optimal" offline decisions, before the true values of the random variables can be observed. We consider three possible concepts of optimality: minimizing the expected cost, minimizing the worst-case cost, or maximizing the probability of a-posteriori optimality. We propose a new family of StochDCOP algorithms, exploring the tradeoffs between solution quality, computational and message complexity, and privacy. In particular, we show how discovering and reasoning about co-dependencies on common random variables can yield higher-quality solutions

Contributions to the energy management of industrial refrigeration systems: a data-driven perspective

Nowadays, energy management has gained attention due to the constant increment of energy consumption in industry and the pollution problems that this fact supposes. On this subject, one of the main industrial sectors, the food and beverage, attributes a great percentage of its energy expenditure to the refrigeration systems. Such systems are highly affected by operation conditions and are commonly composed by different machines that are continually interacting. These particularities difficult the successful application of efficient energy management methodologies requiring further research efforts in order to improve the current approaches. In this regard, with the current framework of the Industry 4.0, the manufacturing industry is moving towards a complete digitalization of its process information. Is in this context, where the promising capabilities of the data-driven techniques can be applied to energy management. Such technology can push forward the energy management to new horizons, since these techniques take advantage of the common data acquired in the refrigeration systems for its inner operation to develop new methodologies able to reach higher efficiencies. Accordingly, this thesis focuses its attention on the research of novel energy management methodologies applied to refrigeration systems by means of data-driven strategies. To address this broad topic and with the aim to improve the efficiency of the industrial refrigeration systems, the current thesis considers three main aspects of any energy management methodology: the system performance assessment, the machinery operation improvement and the load management. Therefore, this thesis presents a novel methodology for each one of the three main aspects considered. The proposed methodologies should contemplate the necessary robustness and reliability to be applicable in real refrigeration systems. The experimental results obtained from the validation tests in the industrial refrigeration system, show the significant improvement capabilities in regard with the energy efficiency. Each one of the proposed methodologies present a promising result and can be employed individually or as a whole, composing a great basis for a data-driven based energy management framework.Avui en dia la gestió energètica ha guanyat interès degut a l'increment constant de consum per part de la indústria i els problemes de contaminació que això suposa. En aquest tema, un dels principals sectors industrials, el d'alimentació i begudes, atribueix bona part de percentatge del seu consum als sistemes de refrigeració. Aquests sistemes es veuen altament afectats per les condicions d'operació i habitualment estan formats per diverses màquines que estan continuament interactuant. Aquestes particularitats dificulten l'aplicació exitosa de metodologies d'eficiència energètica, requerint més esforços en recerca per millorar els enfocs actuals. En aquest tema, amb l'actual marc de la Indústria 4.0, la indústria està avançant cap una digitalització total de la informació dels seus processos. És en aquest context, on les capacitats prometedores de les tècniques basades en dades poden ser aplicades per a la gestió energètica. Aquesta tecnologia pot impulsar la gestió energètica cap a nous horitzons, ja que aquestes tècniques aprofiten les dades adquirides usualment en els sistemes de refrigeració per el seu propi funcionament, per a desenvolupar noves metodologies capaces d'obtenir eficiències més elevades. En conseqüència, aquesta tesi centra la seva atenció en la recerca de noves metodologies per a la gestió energètica, aplicades als sistemes de refrigeració i mitjançant estratègies basades en dades. Per abordar aquest ampli tema i amb el propòsit de millorar l'eficiència dels sistemes de refrigeració industrial, la present tesi considera els tres aspectes principals de qualsevol metodologia de gestió energètica: l'avaluació del rendiment del sistema, la millora de l'operació de la maquinària i la gestió de les càrregues. Per tant, aquesta tesi presenta una metodologia nova per a cadascun dels tres aspectes considerats. Les metodologies proposades han de contemplar la robustesa i fiabilitat necessàries per a ser aplicades en un sistema de refrigeració real. Els resultats experimentals obtinguts dels tests de validació fets en un sistema de refrigeració industrial mostren unes capacitats de millora significatives referent a l'eficiència energètica. Cadascuna de les metodologies proposades presenta un resultat prometedor i pot ser aplicada independentment o juntament amb les altres, formant una bona base per un marc de gestió energètica basat en dades

Agent-Based Algorithms for the Vehicle Routing Problem with Time Windows

Vehicle routing problem s casovymi okny (VRPTW) je jednim z nejdulezitSjSich a nejvice zkou- manych problemu v oblasti dopravy. Matematicky model tohoto problemu vystihuje klicove vlastnosti spolecne cele fadS dalslch dopravmch problemu feSenych v praxi. Jadrem problemu je hledani mnoziny tras zacmajicicli a koncicich v jedinem depu, ktere obsahuji zastavky u mnoziny zakazniku. Pro kazdSho zakazm'ka je pak definovano konkretm' mnozstvf zbozf, jez je tfeba dorucit a casove okno, ve kterem je pozadovano dodani tohoto zbozi. Realne aplikace tohoto problemu jsou zpravidla vyrazne bohatsi, napojene na nadfazene logisticke systemy. KliSoA'ym faktorem pro uspSSne nasazeni odpovldajicich algoritmu je proto jejich fiexibilita vzhledem k dodatecnym rozSuemm zhkladmho matematickeho modelu spojenym s nasazenim v realnem sv§t§. Dalglm podstatnym faktorem je schopnost systemu reagovat na nepfedvidane udalosti jako jsou dopravm zaepy, poruchy, zmgny preferenci zakazniku atd. Multi-agentni systemy reprezentuji architekturu a navrhovy vzor vhodny pro modelovani heterogennlch a dynamickych systemu. Entity v systemu jsou v ramci multi-agentmho mo- delu reprezentovany mnozinou agentil s odpovidajlci'mi vzorci autonommho jako i spolecenskeho chovani. Chovani systemu jako celku pak vyplyva z autonomnich akci...The vehicle routing problem with time windows (VRPTW) is one of the most important and widely studied transportation optimization problems. It abstracts the salient features of numer- ous distribution related real-world problems. It is a problem of finding a set of routes starting and ending at a single depot serving a set of geographically scattered customers, each within a specific time-window and with a specific demand of goods to be delivered. The real world applications of the VRPTW can be very complex being part of higher level sj'^stems i.e. complex supply chain management solutions. For a successful deployment it is impor- tant for these systems to be flexible in terms of incorporating the problem specific side-constraints and problem extensions in an elegant way. Also, employing efficient means of addressing the dy- namism inherent to the execution phase of the relevant operations is vital. The multi-agent systems are an emerging architectm-e with respect to modeling multi-actor heterogenous and dynamic environments. The entities within the system are represented by a set of agents endowed with autonomic as well as social behavioral patterns. The behavior of the system then emerges from their actions and interactions. The autonomic nature of such a model makes it very robust in highly...Katedra softwarového inženýrstvíDepartment of Software EngineeringFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency