Simulation Based Bayesian Optimization

Bayesian Optimization (BO) is a powerful method for optimizing black-box functions by combining prior knowledge with ongoing function evaluations. BO constructs a probabilistic surrogate model of the objective function given the covariates, which is in turn used to inform the selection of future evaluation points through an acquisition function. For smooth continuous search spaces, Gaussian Processes (GPs) are commonly used as the surrogate model as they offer analytical access to posterior predictive distributions, thus facilitating the computation and optimization of acquisition functions. However, in complex scenarios involving optimizations over categorical or mixed covariate spaces, GPs may not be ideal. This paper introduces Simulation Based Bayesian Optimization (SBBO) as a novel approach to optimizing acquisition functions that only requires \emph{sampling-based} access to posterior predictive distributions. SBBO allows the use of surrogate probabilistic models tailored for combinatorial spaces with discrete variables. Any Bayesian model in which posterior inference is carried out through Markov chain Monte Carlo can be selected as the surrogate model in SBBO. In applications involving combinatorial optimization, we demonstrate empirically the effectiveness of SBBO method using various choices of surrogate models

Contribuciones a la Seguridad del Aprendizaje Automático

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Matemáticas, leída el 05-11-2020Machine learning (ML) applications have experienced an unprecedented growth over the last two decades. However, the ever increasing adoption of ML methodologies has revealed important security issues. Among these, vulnerabilities to adversarial examples, data instances targeted at fooling ML algorithms, are especially important. Examples abound. For instance, it is relatively easy to fool a spam detector simply misspelling spam words. Obfuscation of malware code can make it seem legitimate. Simply adding stickers to a stop sign could make an autonomous vehicle classify it as a merge sign. Consequences could be catastrophic. Indeed, ML is designed to work in stationary and benign environments. However, in certain scenarios, the presence of adversaries that actively manipulate input datato fool ML systems to attain benefits break such stationarity requirements. Training and operation conditions are not identical anymore. This creates a whole new class of security vulnerabilities that ML systems may face and a new desirable property: adversarial robustness. If we are to trust operations based on ML outputs, it becomes essential that learning systems are robust to such adversarial manipulations...Las aplicaciones del aprendizaje automático o machine learning (ML) han experimentado un crecimiento sin precedentes en las últimas dos décadas. Sin embargo, la adopción cada vez mayor de metodologías de ML ha revelado importantes problemas de seguridad. Entre estos, destacan las vulnerabilidades a ejemplos adversarios, es decir; instancias de datos destinadas a engañar a los algoritmos de ML. Los ejemplos abundan: es relativamente fácil engañar a un detector de spam simplemente escribiendo mal algunas palabras características de los correos basura. La ofuscación de código malicioso (malware) puede hacer que parezca legítimo. Agregando unos parches a una señal de stop, se podría provocar que un vehículo autónomo la reconociese como una señal de dirección obligatoria. Cómo puede imaginar el lector, las consecuencias de estas vulnerabilidades pueden llegar a ser catastróficas. Y es que el machine learning está diseñado para trabajar en entornos estacionarios y benignos. Sin embargo, en ciertos escenarios, la presencia de adversarios que manipulan activamente los datos de entrada para engañar a los sistemas de ML(logrando así beneficios), rompen tales requisitos de estacionariedad. Las condiciones de entrenamiento y operación de los algoritmos ya no son idénticas, quebrándose una de las hipótesis fundamentales del ML. Esto crea una clase completamente nueva de vulnerabilidades que los sistemas basados en el aprendizaje automático deben enfrentar y una nueva propiedad deseable: la robustez adversaria. Si debemos confiaren las operaciones basadas en resultados del ML, es esencial que los sistemas de aprendizaje sean robustos a tales manipulaciones adversarias...Fac. de Ciencias MatemáticasTRUEunpu

Innovadores sociales. Desarrollo de aptitudes innovadoras enfocadas al emprendimiento social (Prometeo 2020)

El informe reseña la evolución, metodología utilizada y resultados del proyecto de innovación educativa

Estado y sociedad en elPerú. Evolución y perspectiva

Sin resume

Reinforcement Learning under Threats

In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. In this paper, we introduce Threatened Markov Decision Processes (TMDPs), which provide a framework to support a decision maker against a potential adversary in RL. Furthermore, we propose a level-$k$ thinking scheme resulting in a new learning framework to deal with TMDPs. After introducing our framework and deriving theoretical results, relevant empirical evidence is given via extensive experiments, showing the benefits of accounting for adversaries while the agent learns.Comment: Extends the verson published at the Proceedings of the AAAI Conference on Artificial Intelligence 33, https://www.aaai.org/ojs/index.php/AAAI/article/view/510

Codimension two polar homogeneous foliations on symmetric spaces of noncompact type

We classify homogeneous polar foliations of codimension two on irreducible symmetric spaces of noncompact type up to orbit equivalence. Any such foliation is either hyperpolar or the canonical extension of a polar homogeneous foliation on a rank one symmetric space.Comment: 26 page

O Projeto Integrado de Arquitetura. Algumas Considerações Metodológicas

This paper is based in my D.Sc. research done at FAU/USP where was surveyed the technological innovations observed in the manufacturing industry and analised the contribution of CAD to improve the integration between design and manufacturing areas. The same subject is developped here for the construction industry, highlighting some applications of CAD in the archictectural project and its contribution to achieve an integrated design environment. The paper begins with the discussion on technology innovation in both, manufacturing industry and construction industry. The main differences are pointed and some concepts are addapted to architectural design. We finish the paper presenting some CAD applications in archictectural design specially its use as a tool for design retrieval and identifying some integration chains that can be implemented with the use of this technologyEste trabalho foi baseado em minha tese de doutorado elaborada na FAUUSP na qual foi desenvolvido um referencial metodológico para o entendimento do processo de inovações tecnológicas experimentado pela indústria de manufatura, analisando a contribuição do PAC - projeto auxiliado por computador (em inglês CAD - computer aided design) para a integração das atividades de projeto e fabricação. Neste artigo è explorado este mesmo tema para a indústria da construção, procurando identificar as contribuições do PAC para o aumento do nível de integração das atividades presentes no projeto de arquitetura. Inicialmente são discutidos alguns conceitos relativos à implantação de sistemas automatizados de produção em indústrias de manufatura e estabelecidas algumas diferenças entre ela e a indústria da construção habitacional; e feitas algumas adaptações (transposições) desses conceitos para a arquitetura. Finalizando, são mostradas algumas possibilidades de integração entre as diversas fases do projeto arquitetônico com o auxílio do PAC. É ressaltado o uso dc PAC tanto como um intrumento de auxílio à representação como de recuperação de informações do projet