Literatura académica sobre el tema "Black-box learning algorithm"
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Artículos de revistas sobre el tema "Black-box learning algorithm"
Hwangbo, Jemin, Christian Gehring, Hannes Sommer, Roland Siegwart y Jonas Buchli. "Policy Learning with an Efficient Black-Box Optimization Algorithm". International Journal of Humanoid Robotics 12, n.º 03 (septiembre de 2015): 1550029. http://dx.doi.org/10.1142/s0219843615500292.
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 completoXiang, Fengtao, Jiahui Xu, Wanpeng Zhang y Weidong Wang. "A Distributed Biased Boundary Attack Method in Black-Box Attack". Applied Sciences 11, n.º 21 (8 de noviembre de 2021): 10479. http://dx.doi.org/10.3390/app112110479.
Texto completoLIU, Yanhe, Michael AFNAN, Vincent CONTIZER, Cynthia RUDIN, Abhishek MISHRA, Julian SAVULESCU y Masoud AFNAN. "Embryo Selection by “Black-Box” Artificial Intelligence: The Ethical and Epistemic Considerations". Fertility & Reproduction 04, n.º 03n04 (septiembre de 2022): 147. http://dx.doi.org/10.1142/s2661318222740590.
Texto completoBausch, Johannes. "Fast Black-Box Quantum State Preparation". Quantum 6 (4 de agosto de 2022): 773. http://dx.doi.org/10.22331/q-2022-08-04-773.
Texto completoMIKE, KOBY y ORIT HAZZAN. "MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH". STATISTICS EDUCATION RESEARCH JOURNAL 21, n.º 2 (4 de julio de 2022): 10. http://dx.doi.org/10.52041/serj.v21i2.45.
Texto completoGarcía, Javier, Roberto Iglesias, Miguel A. Rodríguez y Carlos V. Regueiro. "Directed Exploration in Black-Box Optimization for Multi-Objective Reinforcement Learning". International Journal of Information Technology & Decision Making 18, n.º 03 (mayo de 2019): 1045–82. http://dx.doi.org/10.1142/s0219622019500093.
Texto completoMayr, Franz, Sergio Yovine y Ramiro Visca. "Property Checking with Interpretable Error Characterization for Recurrent Neural Networks". Machine Learning and Knowledge Extraction 3, n.º 1 (12 de febrero de 2021): 205–27. http://dx.doi.org/10.3390/make3010010.
Texto completoAnđelić, Nikola, Ivan Lorencin, Matko Glučina y Zlatan Car. "Mean Phase Voltages and Duty Cycles Estimation of a Three-Phase Inverter in a Drive System Using Machine Learning Algorithms". Electronics 11, n.º 16 (21 de agosto de 2022): 2623. http://dx.doi.org/10.3390/electronics11162623.
Texto completoVeugen, Thijs, Bart Kamphorst y Michiel Marcus. "Privacy-Preserving Contrastive Explanations with Local Foil Trees". Cryptography 6, n.º 4 (28 de octubre de 2022): 54. http://dx.doi.org/10.3390/cryptography6040054.
Texto completoTesis sobre el tema "Black-box learning algorithm"
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
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.
CURIA, FRANCESCO. "Explainable clinical decision support system: opening black-box meta-learner algorithm expert's based". Doctoral thesis, 2021. http://hdl.handle.net/11573/1538472.
Texto completoRepický, Jakub. "Evoluční algoritmy a aktivní učení". Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-355988.
Texto completoLibros sobre el tema "Black-box learning algorithm"
Russell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Buscar texto completoRussell, David W. The BOXES Methodology: Black Box Dynamic Control. Springer, 2014.
Buscar texto completoThe BOXES Methodology: Black Box Dynamic Control. Springer, 2012.
Buscar texto completoRussell, David W. BOXES Methodology Second Edition: Black Box Control of Ill-Defined Systems. Springer International Publishing AG, 2022.
Buscar texto completoCapítulos de libros sobre el tema "Black-box learning algorithm"
He, Yaodong y Shiu Yin Yuen. "Black Box Algorithm Selection by Convolutional Neural Network". En Machine Learning, Optimization, and Data Science, 264–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64580-9_23.
Texto completoNeele, Thomas y Matteo Sammartino. "Compositional Automata Learning of Synchronous Systems". En Fundamental Approaches to Software Engineering, 47–66. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30826-0_3.
Texto completoCowley, Benjamin Ultan, Darryl Charles, Gerit Pfuhl y Anna-Mari Rusanen. "Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game: A Thought Experiment on AI Ethics". En AI in Learning: Designing the Future, 297–316. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09687-7_18.
Texto completoBaniecki, Hubert, Wojciech Kretowicz y Przemyslaw Biecek. "Fooling Partial Dependence via Data Poisoning". En Machine Learning and Knowledge Discovery in Databases, 121–36. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_8.
Texto completoKlein, Alexander. "Challenges of Model Predictive Control in a Black Box Environment". En Reinforcement Learning Algorithms: Analysis and Applications, 177–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-41188-6_15.
Texto completoCoello, Carlos A. Coello, Silvia González Brambila, Josué Figueroa Gamboa y Ma Guadalupe Castillo Tapia. "Multi-Objective Evolutionary Algorithms: Past, Present, and Future". En Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, 137–62. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66515-9_5.
Texto completoBastani, Osbert, Jeevana Priya Inala y Armando Solar-Lezama. "Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis". En xxAI - Beyond Explainable AI, 207–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_11.
Texto completoBartz-Beielstein, Thomas, Frederik Rehbach y Margarita Rebolledo. "Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives". En Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, 67–108. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66515-9_3.
Texto completoVidovic, Marina M. C., Nico Görnitz, Klaus-Robert Müller, Gunnar Rätsch y Marius Kloft. "Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms". En Machine Learning and Knowledge Discovery in Databases, 137–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_9.
Texto completoSchneider, Lennart, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann y Pascal Kerschke. "HPO $$\times $$ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis". En Lecture Notes in Computer Science, 575–89. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_40.
Texto completoActas de conferencias sobre el tema "Black-box learning algorithm"
Cohen, Itay, Roi Fogler y Doron Peled. "A Reinforcement-Learning Style Algorithm for Black Box Automata". En 2022 20th ACM-IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE). IEEE, 2022. http://dx.doi.org/10.1109/memocode57689.2022.9954382.
Texto completoZhao, Mengchen, Bo An, Wei Gao y Teng Zhang. "Efficient Label Contamination Attacks Against Black-Box Learning Models". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/551.
Texto completoGajane, Pratik, Peter Auer y Ronald Ortner. "Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/413.
Texto completoLiu, Fei-Yu, Zi-Niu Li y Chao Qian. "Self-Guided Evolution Strategies with Historical Estimated Gradients". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/205.
Texto completoSabbatini, Federico y Roberta Calegari. "Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO". En 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/57.
Texto completoSantos, Samara Silva, Marcos Antonio Alves, Leonardo Augusto Ferreira y Frederico Gadelha Guimarães. "PDTX: A novel local explainer based on the Perceptron Decision Tree". En Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-50.
Texto completoRussell, David W. "On the Control of Dynamically Unstable Systems Using a Self Organizing Black Box Controller". En ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58290.
Texto completoAbba, S. I., Sagir Jibrin Kawu, Hamza Sabo Maccido, S. M. Lawan, Gafai Najashi y Abdullahi Yusuf Sada. "Short-term load demand forecasting using nonlinear dynamic grey-black-box and kernel optimization models: a new generation learning algorithm". En 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS). IEEE, 2021. http://dx.doi.org/10.1109/icmeas52683.2021.9692314.
Texto completoHeidari, Hoda y Andreas Krause. "Preventing Disparate Treatment in Sequential Decision Making". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/311.
Texto completoZhao, Jiangjiang, Zhuoran Wang y Fangchun Yang. "Genetic Prompt Search via Exploiting Language Model Probabilities". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/588.
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