Relevant bibliographies by topics / Black-box learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Black-box learning'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Black-box learning"

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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.

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Battaile, Bennett. "Black-box electronics and passive learning." Physics Today 67, no. 2 (2014): 11. http://dx.doi.org/10.1063/pt.3.2258.

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Hess, Karl. "Black-box electronics and passive learning." Physics Today 67, no. 2 (2014): 11–12. http://dx.doi.org/10.1063/pt.3.2259.

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Katrutsa, 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.

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The Lancet Respiratory Medicine. "Opening the black box of machine learning." Lancet Respiratory Medicine 6, no. 11 (2018): 801. http://dx.doi.org/10.1016/s2213-2600(18)30425-9.

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Rudnick, Abraham. "The Black Box Myth." International Journal of Extreme Automation and Connectivity in Healthcare 1, no. 1 (2019): 1–3. http://dx.doi.org/10.4018/ijeach.2019010101.

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Artificial intelligence (AI) and its correlates, such as machine and deep learning, are changing health care, where complex matters such as comoribidity call for dynamic decision-making. Yet, some people argue for extreme caution, referring to AI and its correlates as a black box. This brief article uses philosophy and science to address the black box argument about knowledge as a myth, concluding that this argument is misleading as it ignores a fundamental tenet of science, i.e., that no empirical knowledge is certain, and that scientific facts – as well as methods – often change. Instead, co
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Pintelas, 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 (2020): 17. http://dx.doi.org/10.3390/a13010017.

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Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order
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Kirsch, 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 (2022): 7202–10. http://dx.doi.org/10.1609/aaai.v36i7.20681.

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Meta reinforcement learning (RL) attempts to discover new RL algorithms automatically from environment interaction. In so-called black-box approaches, the policy and the learning algorithm are jointly represented by a single neural network. These methods are very flexible, but they tend to underperform compared to human-engineered RL algorithms in terms of generalisation to new, unseen environments. In this paper, we explore the role of symmetries in meta-generalisation. We show that a recent successful meta RL approach that meta-learns an objective for backpropagation-based learning exhibits
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Taub, Simon, and Oleg S. Pianykh. "An alternative to the black box: Strategy learning." PLOS ONE 17, no. 3 (2022): e0264485. http://dx.doi.org/10.1371/journal.pone.0264485.

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In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes. Workflow planning is one of the most common and important problems of this kind, as sub-optimal decision-making may create bottlenecks and delays that decrease efficiency and increase costs. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are “black box” algorithms with underlying logic unclear to humans. This makes them hard to implement and impossible to trust, significantly limi
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Hargreaves, Eleanore. "Assessment for learning? Thinking outside the (black) box." Cambridge Journal of Education 35, no. 2 (2005): 213–24. http://dx.doi.org/10.1080/03057640500146880.

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Dissertations / Theses on the topic "Black-box learning"

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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.

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Application of neural networks in deep learning is rapidly growing due to their ability to outperform other machine learning algorithms in different kinds of problems. But one big disadvantage of deep neural networks is its internal logic to achieve the desired output or result that is un-understandable and unexplainable. This behavior of the deep neural network is known as “black box”. This leads to the following questions: how prevalent is the black box problem in the research literature during a specific period of time? The black box problems are usually addressed by socalled rule extractio
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Kamp, Michael [Verfasser]. "Black-Box Parallelization for Machine Learning / Michael Kamp." Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/1200020057/34.

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Verì, Daniele. "Empirical Model Learning for Constrained Black Box Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25704/.

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Black box optimization is a field of the global optimization which consists in a family of methods intended to minimize or maximize an objective function that doesn’t allow the exploitation of gradients, linearity or convexity information. Beside that the objective is often a problem that requires a significant amount of time/resources to query a point and thus the goal is to go as close as possible to the optimum with the less number of iterations possible. The Emprical Model Learning is a methodology for merging Machine Learning and optimization techniques like Constraint Programming
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Rowan, Adriaan. "Unravelling black box machine learning methods using biplots." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31124.

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Following the development of new mathematical techniques, the improvement of computer processing power and the increased availability of possible explanatory variables, the financial services industry is moving toward the use of new machine learning methods, such as neural networks, and away from older methods such as generalised linear models. However, their use is currently limited because they are seen as “black box” models, which gives predictions without justifications and which are therefore not understood and cannot be trusted. The goal of this dissertation is to expand on the theory an
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Mena, Roldán José. "Modelling Uncertainty in Black-box Classification Systems." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670763.

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Currently, thanks to the Big Data boom, the excellent results obtained by deep learning models and the strong digital transformation experienced over the last years, many companies have decided to incorporate machine learning models into their systems. Some companies have detected this opportunity and are making a portfolio of artificial intelligence services available to third parties in the form of application programming interfaces (APIs). Subsequently, developers include calls to these APIs to incorporate AI functionalities in their products. Although it is an option that saves time and re
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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.

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Les problèmes d’optimisation apparaissent dans presque tous les domaines scientifiques. Cependant, le processus laborieux de conception d’un optimiseur approprié peut demeurer infructueux. La question la plus ambitieuse de l’optimisation est peut-être de savoir comment concevoir des optimiseurs suffisamment flexibles pour s’adapter à un grand nombre de scénarios, tout en atteignant des performances de pointe. Dans ce travail, nous visons donner une réponse potentielle à cette question en étudiant comment faire un méta-apprentissage d’optimiseurs à base de population. Nous motivons et décrivons
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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.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 45-47).<br>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
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Belkhir, Nacim. "Per Instance Algorithm Configuration for Continuous Black Box Optimization." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS455/document.

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Cette thèse porte sur la configurationAutomatisée des algorithmes qui vise à trouver le meilleur paramétrage à un problème donné ou une catégorie deproblèmes.Le problème de configuration de l'algorithme revient doncà un problème de métaFoptimisation dans l'espace desparamètres, dont le métaFobjectif est la mesure deperformance de l’algorithme donné avec une configuration de paramètres donnée.Des approches plus récentes reposent sur une description des problèmes et ont pour but d’apprendre la relationentre l’espace des caractéristiques des problèmes etl’espace des configurations de l’algorithme
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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.

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I recenti algoritmi di apprendimento automatico ad alte prestazioni sono convincenti ma opachi, quindi spesso è difficile capire come arrivano alle loro previsioni, dando origine a problemi di interpretabilità. Questi problemi sono particolarmente rilevanti nell'apprendimento supervisionato, dove questi modelli "black-box" non sono facilmente comprensibili per le parti interessate. Un numero crescente di lavori si concentra sul rendere più interpretabili i modelli di apprendimento automatico, in particolare quelli di apprendimento profondo. Gli approcci attualmente proposti si basano su un'i
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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.

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The thesis investigates how one should determine the appropriate transparency of an information processing system from a receiver perspective. Research in the past has suggested that the model should be maximally transparent for what is labeled as ”high stake decisions”. Instead of motivating the choice of a model’s transparency on the non-rigorous criterion that the model contributes to a high stake decision, this thesis explores an alternative method. The suggested method involves that one should let the transparency depend on how well an explanation of the model’s output satisfies the purpo
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Books on the topic "Black-box learning"

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Group, Assessment Reform, and University of Cambridge. Faculty of Education., eds. Assessment for learning: Beyond the black box. Assessment Reform Group, 1999.

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Pardalos, Panos M., Varvara Rasskazova, and Michael N. Vrahatis, eds. Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66515-9.

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1979-, Nashat Bidjan, and World Bank, eds. The black box of governmental learning: The learning spiral -- a concept to organize learning in governments. World Bank, 2010.

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King's College, London. Department of Education and Professional Studies., ed. Working inside the black box: Assessment for learning in the classroom. nferNelson, 2002.

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1930-, 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. Department of Education and Professional Studies, Kings College, London, 2002.

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Russell, David W. The BOXES Methodology: Black Box Dynamic Control. Springer London, 2012.

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Black, Paul. Working inside the black box: An assessment for learning in the classroom. Department of Education and Professional Studies, Kings College, 2002.

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J, 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. NferNelson, 2007.

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English Inside The Black Box Assessment For Learning In The English Classroom. GL Assessment, 2006.

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Pardalos, P. M. Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer International Publishing AG, 2022.

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Book chapters on the topic "Black-box learning"

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Howard, Sarah, Kate Thompson, and Abelardo Pardo. "Opening the black box." In Learning Analytics in the Classroom. Routledge, 2018. http://dx.doi.org/10.4324/9781351113038-10.

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Dinov, Ivo D. "Black Box Machine Learning Methods." In The Springer Series in Applied Machine Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17483-4_6.

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Sudmann, Andreas. "On Computer creativity. Machine learning and the arts of artificial intelligences." In The Black Box Book. Masaryk University Press, 2022. http://dx.doi.org/10.5817/cz.muni.m280-0225-2022-11.

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Fournier-Viger, Philippe, Mehdi Najjar, André Mayers, and Roger Nkambou. "From Black-Box Learning Objects to Glass-Box Learning Objects." In Intelligent Tutoring Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11774303_26.

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TV, Vishnu, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, and Gautam Shroff. "Meta-Learning for Black-Box Optimization." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46147-8_22.

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Archetti, 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66515-9_1.

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Kampakis, Stylianos. "Machine Learning: Inside the Black Box." In Predicting the Unknown. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9505-2_8.

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Stachowiak-Szymczak, Katarzyna. "Interpreting: Different Approaches Towards the ‘Black Box’." In Second Language Learning and Teaching. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19443-7_1.

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Cai, 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. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36204-1_33.

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Kuri-Morales, Angel Fernando. "Removing the Black-Box from Machine Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33783-3_4.

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Conference papers on the topic "Black-box learning"

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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}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/330.

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We study two user demands that are important during the exploitation of explanations in practice: 1) understanding the overall model behavior faithfully with limited cognitive load and 2) predicting the model behavior accurately on unseen instances. We illustrate that the two user demands correspond to two major sub-processes in the human cognitive process and propose a unified framework to fulfill them simultaneously. Given a local explanation method, our framework jointly 1) learns a limited number of groupwise explanations that interpret the model behavior on most instances with high fideli
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Papernot, 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. ACM, 2017. http://dx.doi.org/10.1145/3052973.3053009.

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Wajahat, 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.

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Aggarwal, 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. ACM, 2019. http://dx.doi.org/10.1145/3338906.3338937.

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Rasouli, 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.

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Pengcheng, 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.

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Nikoloska, 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.

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Fu, 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.

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Huang, 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.

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Han, 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.

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Reports on the topic "Black-box learning"

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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), 2022. http://dx.doi.org/10.2172/1905375.

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Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202305.

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In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference us
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