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Статті в журналах з теми "Black-box learning"
Nax, Heinrich H., Maxwell N. Burton-Chellew, Stuart A. West, and H. Peyton Young. "Learning in a black box." Journal of Economic Behavior & Organization 127 (July 2016): 1–15. http://dx.doi.org/10.1016/j.jebo.2016.04.006.
Повний текст джерелаBattaile, Bennett. "Black-box electronics and passive learning." Physics Today 67, no. 2 (February 2014): 11. http://dx.doi.org/10.1063/pt.3.2258.
Повний текст джерелаHess, Karl. "Black-box electronics and passive learning." Physics Today 67, no. 2 (February 2014): 11–12. http://dx.doi.org/10.1063/pt.3.2259.
Повний текст джерелаKatrutsa, Alexandr, Talgat Daulbaev, and Ivan Oseledets. "Black-box learning of multigrid parameters." Journal of Computational and Applied Mathematics 368 (April 2020): 112524. http://dx.doi.org/10.1016/j.cam.2019.112524.
Повний текст джерелаThe Lancet Respiratory Medicine. "Opening the black box of machine learning." Lancet Respiratory Medicine 6, no. 11 (November 2018): 801. http://dx.doi.org/10.1016/s2213-2600(18)30425-9.
Повний текст джерелаRudnick, Abraham. "The Black Box Myth." International Journal of Extreme Automation and Connectivity in Healthcare 1, no. 1 (January 2019): 1–3. http://dx.doi.org/10.4018/ijeach.2019010101.
Повний текст джерелаPintelas, Emmanuel, Ioannis E. Livieris, and Panagiotis Pintelas. "A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability." Algorithms 13, no. 1 (January 5, 2020): 17. http://dx.doi.org/10.3390/a13010017.
Повний текст джерелаKirsch, Louis, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, and Yutian Chen. "Introducing Symmetries to Black Box Meta Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7202–10. http://dx.doi.org/10.1609/aaai.v36i7.20681.
Повний текст джерелаTaub, Simon, and Oleg S. Pianykh. "An alternative to the black box: Strategy learning." PLOS ONE 17, no. 3 (March 18, 2022): e0264485. http://dx.doi.org/10.1371/journal.pone.0264485.
Повний текст джерелаHargreaves, Eleanore. "Assessment for learning? Thinking outside the (black) box." Cambridge Journal of Education 35, no. 2 (June 2005): 213–24. http://dx.doi.org/10.1080/03057640500146880.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаKamp, Michael [Verfasser]. "Black-Box Parallelization for Machine Learning / Michael Kamp." Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/1200020057/34.
Повний текст джерелаVerì, Daniele. "Empirical Model Learning for Constrained Black Box Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25704/.
Повний текст джерелаRowan, Adriaan. "Unravelling black box machine learning methods using biplots." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31124.
Повний текст джерелаMena, Roldán José. "Modelling Uncertainty in Black-box Classification Systems." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670763.
Повний текст джерелаLa 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.
Повний текст джерелаOptimization 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.
Повний текст джерелаThesis: 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.
Повний текст джерелаThis 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.
Повний текст джерелаRecent 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.
Повний текст джерелаКниги з теми "Black-box learning"
Group, Assessment Reform, and University of Cambridge. Faculty of Education., eds. Assessment for learning: Beyond the black box. [Cambridge?]: Assessment Reform Group, 1999.
Знайти повний текст джерелаPardalos, Panos M., Varvara Rasskazova, and 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.
Повний текст джерела1979-, Nashat Bidjan, and 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.
Знайти повний текст джерелаKing's College, London. Department of Education and Professional Studies., ed. Working inside the black box: Assessment for learning in the classroom. London: nferNelson, 2002.
Знайти повний текст джерела1930-, Black P. J., and 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.
Знайти повний текст джерелаRussell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Знайти повний текст джерелаBlack, Paul. Working inside the black box: An assessment for learning in the classroom. London: Department of Education and Professional Studies, Kings College, 2002.
Знайти повний текст джерелаJ, Cox Margaret, and 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.
Знайти повний текст джерелаEnglish Inside The Black Box Assessment For Learning In The English Classroom. GL Assessment, 2006.
Знайти повний текст джерелаPardalos, P. M. Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer International Publishing AG, 2022.
Знайти повний текст джерелаЧастини книг з теми "Black-box learning"
Howard, Sarah, Kate Thompson, and Abelardo Pardo. "Opening the black box." In Learning Analytics in the Classroom, 152–64. Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9781351113038-10.
Повний текст джерелаDinov, Ivo D. "Black Box Machine Learning Methods." In 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.
Повний текст джерелаSudmann, Andreas. "On Computer creativity. Machine learning and the arts of artificial intelligences." In The Black Box Book, 264–80. Brno: Masaryk University Press, 2022. http://dx.doi.org/10.5817/cz.muni.m280-0225-2022-11.
Повний текст джерелаFournier-Viger, Philippe, Mehdi Najjar, André Mayers, and Roger Nkambou. "From Black-Box Learning Objects to Glass-Box Learning Objects." In Intelligent Tutoring Systems, 258–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11774303_26.
Повний текст джерелаTV, Vishnu, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, and Gautam Shroff. "Meta-Learning for Black-Box Optimization." In 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.
Повний текст джерелаArchetti, F., A. Candelieri, B. G. Galuzzi, and R. Perego. "Learning Enabled Constrained Black-Box Optimization." In 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.
Повний текст джерелаKampakis, Stylianos. "Machine Learning: Inside the Black Box." In Predicting the Unknown, 113–31. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9505-2_8.
Повний текст джерелаStachowiak-Szymczak, Katarzyna. "Interpreting: Different Approaches Towards the ‘Black Box’." In Second Language Learning and Teaching, 1–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19443-7_1.
Повний текст джерелаCai, Jinghui, Boyang Wang, Xiangfeng Wang, and Bo Jin. "Accelerate Black-Box Attack with White-Box Prior Knowledge." In 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.
Повний текст джерелаKuri-Morales, Angel Fernando. "Removing the Black-Box from Machine Learning." In Lecture Notes in Computer Science, 36–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33783-3_4.
Повний текст джерелаТези доповідей конференцій з теми "Black-box learning"
Gao, Jingyue, Xiting Wang, Yasha Wang, Yulan Yan, and Xing Xie. "Learning Groupwise Explanations for Black-Box Models." In 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.
Повний текст джерелаPapernot, Nicolas, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami. "Practical Black-Box Attacks against Machine Learning." In 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.
Повний текст джерелаWajahat, Muhammad, Anshul Gandhi, Alexei Karve, and Andrzej Kochut. "Using machine learning for black-box autoscaling." In 2016 Seventh International Green and Sustainable Computing Conference (IGSC). IEEE, 2016. http://dx.doi.org/10.1109/igcc.2016.7892598.
Повний текст джерелаAggarwal, Aniya, Pranay Lohia, Seema Nagar, Kuntal Dey, and Diptikalyan Saha. "Black box fairness testing of machine learning models." In 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.
Повний текст джерелаRasouli, Peyman, and Ingrid Chieh Yu. "Explainable Debugger for Black-box Machine Learning Models." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533944.
Повний текст джерелаPengcheng, Li, Jinfeng Yi, and Lijun Zhang. "Query-Efficient Black-Box Attack by Active Learning." In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00159.
Повний текст джерелаNikoloska, Ivana, and Osvaldo Simeone. "Bayesian Active Meta-Learning for Black-Box Optimization." In 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC). IEEE, 2022. http://dx.doi.org/10.1109/spawc51304.2022.9833993.
Повний текст джерелаFu, Junjie, Jian Sun, and Gang Wang. "Boosting Black-Box Adversarial Attacks with Meta Learning." In 2022 41st Chinese Control Conference (CCC). IEEE, 2022. http://dx.doi.org/10.23919/ccc55666.2022.9901576.
Повний текст джерелаHuang, Chen, Shuangfei Zhai, Pengsheng Guo, and Josh Susskind. "MetricOpt: Learning to Optimize Black-Box Evaluation Metrics." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00024.
Повний текст джерелаHan, Gyojin, Jaehyun Choi, Haeil Lee, and Junmo Kim. "Reinforcement Learning-Based Black-Box Model Inversion Attacks." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01964.
Повний текст джерелаЗвіти організацій з теми "Black-box learning"
Zhang, Guannan, Matt Bement, and Hoang Tran. Final Report on Field Work Proposal ERKJ358: Black-Box Training for Scientific Machine Learning Models. Office of Scientific and Technical Information (OSTI), December 2022. http://dx.doi.org/10.2172/1905375.
Повний текст джерелаHauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, January 2023. http://dx.doi.org/10.26509/frbc-wp-202305.
Повний текст джерела