Literatura académica sobre el tema "Black-box learning"
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Artículos de revistas sobre el tema "Black-box learning"
Nax, Heinrich H., Maxwell N. Burton-Chellew, Stuart A. West y H. Peyton Young. "Learning in a black box". Journal of Economic Behavior & Organization 127 (julio de 2016): 1–15. http://dx.doi.org/10.1016/j.jebo.2016.04.006.
Texto completoBattaile, Bennett. "Black-box electronics and passive learning". Physics Today 67, n.º 2 (febrero de 2014): 11. http://dx.doi.org/10.1063/pt.3.2258.
Texto completoHess, Karl. "Black-box electronics and passive learning". Physics Today 67, n.º 2 (febrero de 2014): 11–12. http://dx.doi.org/10.1063/pt.3.2259.
Texto completoKatrutsa, Alexandr, Talgat Daulbaev y Ivan Oseledets. "Black-box learning of multigrid parameters". Journal of Computational and Applied Mathematics 368 (abril de 2020): 112524. http://dx.doi.org/10.1016/j.cam.2019.112524.
Texto completoThe Lancet Respiratory Medicine. "Opening the black box of machine learning". Lancet Respiratory Medicine 6, n.º 11 (noviembre de 2018): 801. http://dx.doi.org/10.1016/s2213-2600(18)30425-9.
Texto completoRudnick, Abraham. "The Black Box Myth". International Journal of Extreme Automation and Connectivity in Healthcare 1, n.º 1 (enero de 2019): 1–3. http://dx.doi.org/10.4018/ijeach.2019010101.
Texto completoPintelas, Emmanuel, Ioannis E. Livieris y Panagiotis Pintelas. "A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability". Algorithms 13, n.º 1 (5 de enero de 2020): 17. http://dx.doi.org/10.3390/a13010017.
Texto completoKirsch, Louis, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh y Yutian Chen. "Introducing Symmetries to Black Box Meta Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junio de 2022): 7202–10. http://dx.doi.org/10.1609/aaai.v36i7.20681.
Texto completoTaub, Simon y Oleg S. Pianykh. "An alternative to the black box: Strategy learning". PLOS ONE 17, n.º 3 (18 de marzo de 2022): e0264485. http://dx.doi.org/10.1371/journal.pone.0264485.
Texto completoHargreaves, Eleanore. "Assessment for learning? Thinking outside the (black) box". Cambridge Journal of Education 35, n.º 2 (junio de 2005): 213–24. http://dx.doi.org/10.1080/03057640500146880.
Texto completoTesis sobre el tema "Black-box learning"
Hussain, Jabbar. "Deep Learning Black Box Problem". Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393479.
Texto completoKamp, Michael [Verfasser]. "Black-Box Parallelization for Machine Learning / Michael Kamp". Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/1200020057/34.
Texto completoVerì, Daniele. "Empirical Model Learning for Constrained Black Box Optimization". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25704/.
Texto completoRowan, Adriaan. "Unravelling black box machine learning methods using biplots". Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31124.
Texto completoMena, Roldán José. "Modelling Uncertainty in Black-box Classification Systems". Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670763.
Texto completoLa tesis propone un método para el cálculo de la incertidumbre asociada a las predicciones de APIs o librerías externas de sistemas de clasificación.
Siqueira, Gomes Hugo. "Meta learning for population-based algorithms in black-box optimization". Master's thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/68764.
Texto completoOptimization problems appear in almost any scientific field. However, the laborious process to design a suitable optimizer may lead to an unsuccessful outcome. Perhaps the most ambitious question in optimization is how we can design optimizers that can be flexible enough to adapt to a vast number of scenarios while at the same time reaching state-of-the-art performance. In this work, we aim to give a potential answer to this question by investigating how to metalearn population-based optimizers. We motivate and describe a common structure for most population-based algorithms, which present principles for general adaptation. This structure can derive a meta-learning framework based on a Partially observable Markov decision process (POMDP). Our conceptual formulation provides a general methodology to learn the optimizer algorithm itself, framed as a meta-learning or learning-to-optimize problem using black-box benchmarking datasets to train efficient general-purpose optimizers. We estimate a meta-loss training function based on stochastic algorithms’ performance. Our experimental analysis indicates that this new meta-loss function encourages the learned algorithm to be sample efficient and robust to premature convergence. Besides, we show that our approach can alter an algorithm’s search behavior to fit easily in a new context and be sample efficient compared to state-of-the-art algorithms, such as CMA-ES.
Sun, Michael(Michael Z. ). "Local approximations of deep learning models for black-box adversarial attacks". Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121687.
Texto completoThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 45-47).
We study the problem of generating adversarial examples for image classifiers in the black-box setting (when the model is available only as an oracle). We unify two seemingly orthogonal and concurrent lines of work in black-box adversarial generation: query-based attacks and substitute models. In particular, we reinterpret adversarial transferability as a strong gradient prior. Based on this unification, we develop a method for integrating model-based priors into the generation of black-box attacks. The resulting algorithms significantly improve upon the current state-of-the-art in black-box adversarial attacks across a wide range of threat models.
by Michael Sun.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Belkhir, Nacim. "Per Instance Algorithm Configuration for Continuous Black Box Optimization". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS455/document.
Texto completoThis PhD thesis focuses on the automated algorithm configuration that aims at finding the best parameter setting for a given problem or a' class of problem. The Algorithm Configuration problem thus amounts to a metal Foptimization problem in the space of parameters, whosemetaFobjective is the performance measure of the given algorithm at hand with a given parameter configuration. However, in the continuous domain, such method can only be empirically assessed at the cost of running the algorithm on some problem instances. More recent approaches rely on a description of problems in some features space, and try to learn a mapping from this feature space onto the space of parameter configurations of the algorithm at hand. Along these lines, this PhD thesis focuses on the Per Instance Algorithm Configuration (PIAC) for solving continuous black boxoptimization problems, where only a limited budget confessionnalisations available. We first survey Evolutionary Algorithms for continuous optimization, with a focus on two algorithms that we have used as target algorithm for PIAC, DE and CMAFES. Next, we review the state of the art of Algorithm Configuration approaches, and the different features that have been proposed in the literature to describe continuous black box optimization problems. We then introduce a general methodology to empirically study PIAC for the continuous domain, so that all the components of PIAC can be explored in real Fworld conditions. To this end, we also introduce a new continuous black box test bench, distinct from the famous BBOB'benchmark, that is composed of a several multiFdimensional test functions with different problem properties, gathered from the literature. The methodology is finally applied to two EAS. First we use Differential Evolution as'target algorithm, and explore all the components of PIAC, such that we empirically assess the best. Second, based on the results on DE, we empirically investigate PIAC with Covariance Matrix Adaptation Evolution Strategy (CMAFES) as target algorithm. Both use cases empirically validate the proposed methodology on the new black box testbench for dimensions up to100
REPETTO, MARCO. "Black-box supervised learning and empirical assessment: new perspectives in credit risk modeling". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/402366.
Texto completoRecent highly performant Machine Learning algorithms are compelling but opaque, so it is often hard to understand how they arrive at their predictions giving rise to interpretability issues. Such issues are particularly relevant in supervised learning, where such black-box models are not easily understandable by the stakeholders involved. A growing body of work focuses on making Machine Learning, particularly Deep Learning models, more interpretable. The currently proposed approaches rely on post-hoc interpretation, using methods such as saliency mapping and partial dependencies. Despite the advances that have been made, interpretability is still an active area of research, and there is no silver bullet solution. Moreover, in high-stakes decision-making, post-hoc interpretability may be sub-optimal. An example is the field of enterprise credit risk modeling. In such fields, classification models discriminate between good and bad borrowers. As a result, lenders can use these models to deny loan requests. Loan denial can be especially harmful when the borrower cannot appeal or have the decision explained and grounded by fundamentals. Therefore in such cases, it is crucial to understand why these models produce a given output and steer the learning process toward predictions based on fundamentals. This dissertation focuses on the concept of Interpretable Machine Learning, with particular attention to the context of credit risk modeling. In particular, the dissertation revolves around three topics: model agnostic interpretability, post-hoc interpretation in credit risk, and interpretability-driven learning. More specifically, the first chapter is a guided introduction to the model-agnostic techniques shaping today’s landscape of Machine Learning and their implementations. The second chapter focuses on an empirical analysis of the credit risk of Italian Small and Medium Enterprises. It proposes an analytical pipeline in which post-hoc interpretability plays a crucial role in finding the relevant underpinnings that drive a firm into bankruptcy. The third and last paper proposes a novel multicriteria knowledge injection methodology. The methodology is based on double backpropagation and can improve model performance, especially in the case of scarce data. The essential advantage of such methodology is that it allows the decision maker to impose his previous knowledge at the beginning of the learning process, making predictions that align with the fundamentals.
Joel, Viklund. "Explaining the output of a black box model and a white box model: an illustrative comparison". Thesis, Uppsala universitet, Filosofiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420889.
Texto completoLibros sobre el tema "Black-box learning"
Group, Assessment Reform y University of Cambridge. Faculty of Education., eds. Assessment for learning: Beyond the black box. [Cambridge?]: Assessment Reform Group, 1999.
Buscar texto completoPardalos, Panos M., Varvara Rasskazova y Michael N. Vrahatis, eds. Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66515-9.
Texto completo1979-, Nashat Bidjan y World Bank, eds. The black box of governmental learning: The learning spiral -- a concept to organize learning in governments. Washington, D.C: World Bank, 2010.
Buscar texto completoKing's College, London. Department of Education and Professional Studies., ed. Working inside the black box: Assessment for learning in the classroom. London: nferNelson, 2002.
Buscar texto completo1930-, Black P. J. y King's College, London. Department of Education and Professional Studies., eds. Working inside the black box: Assessment for learning in the classroom. London: Department of Education and Professional Studies, Kings College, London, 2002.
Buscar texto completoRussell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Buscar texto completoBlack, Paul. Working inside the black box: An assessment for learning in the classroom. London: Department of Education and Professional Studies, Kings College, 2002.
Buscar texto completoJ, Cox Margaret y King's College London. Department of Education and Professional Studies, eds. Information and communication technology inside the black box: Assessment for learning in the ICT classroom. London: NferNelson, 2007.
Buscar texto completoEnglish Inside The Black Box Assessment For Learning In The English Classroom. GL Assessment, 2006.
Buscar texto completoPardalos, P. M. Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer International Publishing AG, 2022.
Buscar texto completoCapítulos de libros sobre el tema "Black-box learning"
Howard, Sarah, Kate Thompson y Abelardo Pardo. "Opening the black box". En Learning Analytics in the Classroom, 152–64. Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9781351113038-10.
Texto completoDinov, Ivo D. "Black Box Machine Learning Methods". En The Springer Series in Applied Machine Learning, 341–83. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17483-4_6.
Texto completoSudmann, Andreas. "On Computer creativity. Machine learning and the arts of artificial intelligences". En The Black Box Book, 264–80. Brno: Masaryk University Press, 2022. http://dx.doi.org/10.5817/cz.muni.m280-0225-2022-11.
Texto completoFournier-Viger, Philippe, Mehdi Najjar, André Mayers y Roger Nkambou. "From Black-Box Learning Objects to Glass-Box Learning Objects". En Intelligent Tutoring Systems, 258–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11774303_26.
Texto completoTV, Vishnu, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig y Gautam Shroff. "Meta-Learning for Black-Box Optimization". En Machine Learning and Knowledge Discovery in Databases, 366–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46147-8_22.
Texto completoArchetti, F., A. Candelieri, B. G. Galuzzi y R. Perego. "Learning Enabled Constrained Black-Box Optimization". En Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, 1–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66515-9_1.
Texto completoKampakis, Stylianos. "Machine Learning: Inside the Black Box". En Predicting the Unknown, 113–31. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9505-2_8.
Texto completoStachowiak-Szymczak, Katarzyna. "Interpreting: Different Approaches Towards the ‘Black Box’". En Second Language Learning and Teaching, 1–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19443-7_1.
Texto completoCai, Jinghui, Boyang Wang, Xiangfeng Wang y Bo Jin. "Accelerate Black-Box Attack with White-Box Prior Knowledge". En Intelligence Science and Big Data Engineering. Big Data and Machine Learning, 394–405. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36204-1_33.
Texto completoKuri-Morales, Angel Fernando. "Removing the Black-Box from Machine Learning". En Lecture Notes in Computer Science, 36–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33783-3_4.
Texto completoActas de conferencias sobre el tema "Black-box learning"
Gao, Jingyue, Xiting Wang, Yasha Wang, Yulan Yan y Xing Xie. "Learning Groupwise Explanations for Black-Box Models". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/330.
Texto completoPapernot, Nicolas, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik y Ananthram Swami. "Practical Black-Box Attacks against Machine Learning". En ASIA CCS '17: ACM Asia Conference on Computer and Communications Security. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3052973.3053009.
Texto completoWajahat, Muhammad, Anshul Gandhi, Alexei Karve y Andrzej Kochut. "Using machine learning for black-box autoscaling". En 2016 Seventh International Green and Sustainable Computing Conference (IGSC). IEEE, 2016. http://dx.doi.org/10.1109/igcc.2016.7892598.
Texto completoAggarwal, Aniya, Pranay Lohia, Seema Nagar, Kuntal Dey y Diptikalyan Saha. "Black box fairness testing of machine learning models". En ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3338906.3338937.
Texto completoRasouli, Peyman y Ingrid Chieh Yu. "Explainable Debugger for Black-box Machine Learning Models". En 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533944.
Texto completoPengcheng, Li, Jinfeng Yi y Lijun Zhang. "Query-Efficient Black-Box Attack by Active Learning". En 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00159.
Texto completoNikoloska, Ivana y Osvaldo Simeone. "Bayesian Active Meta-Learning for Black-Box Optimization". En 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC). IEEE, 2022. http://dx.doi.org/10.1109/spawc51304.2022.9833993.
Texto completoFu, Junjie, Jian Sun y Gang Wang. "Boosting Black-Box Adversarial Attacks with Meta Learning". En 2022 41st Chinese Control Conference (CCC). IEEE, 2022. http://dx.doi.org/10.23919/ccc55666.2022.9901576.
Texto completoHuang, Chen, Shuangfei Zhai, Pengsheng Guo y Josh Susskind. "MetricOpt: Learning to Optimize Black-Box Evaluation Metrics". En 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00024.
Texto completoHan, Gyojin, Jaehyun Choi, Haeil Lee y Junmo Kim. "Reinforcement Learning-Based Black-Box Model Inversion Attacks". En 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01964.
Texto completoInformes sobre el tema "Black-box learning"
Zhang, Guannan, Matt Bement y Hoang Tran. Final Report on Field Work Proposal ERKJ358: Black-Box Training for Scientific Machine Learning Models. Office of Scientific and Technical Information (OSTI), diciembre de 2022. http://dx.doi.org/10.2172/1905375.
Texto completoHauzenberger, Niko, Florian Huber, Gary Koop y James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, enero de 2023. http://dx.doi.org/10.26509/frbc-wp-202305.
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