Relevant bibliographies by topics / Distribution learning theory

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

Academic literature on the topic 'Distribution learning theory'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Distribution learning theory"

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Vidyasagar, M., and S. R. Kulkarni. "Some contributions to fixed-distribution learning theory." IEEE Transactions on Automatic Control 45, no. 2 (2000): 217–34. http://dx.doi.org/10.1109/9.839945.

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Tsutsumi, Emiko, Ryo Kinoshita, and Maomi Ueno. "Deep Item Response Theory as a Novel Test Theory Based on Deep Learning." Electronics 10, no. 9 (2021): 1020. http://dx.doi.org/10.3390/electronics10091020.

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Item Response Theory (IRT) evaluates, on the same scale, examinees who take different tests. It requires the linkage of examinees’ ability scores as estimated from different tests. However, the IRT linkage techniques assume independently random sampling of examinees’ abilities from a standard normal distribution. Because of this assumption, the linkage not only requires much labor to design, but it also has no guarantee of optimality. To resolve that shortcoming, this study proposes a novel IRT based on deep learning, Deep-IRT, which requires no assumption of randomly sampled examinees’ abilit
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Najarian, Kayvan. "A Fixed-Distribution PAC Learning Theory for Neural FIR Models." Journal of Intelligent Information Systems 25, no. 3 (2005): 275–91. http://dx.doi.org/10.1007/s10844-005-0194-y.

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Wang, Jing, and Xin Geng. "Theoretical Analysis of Label Distribution Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5256–63. http://dx.doi.org/10.1609/aaai.v33i01.33015256.

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As a novel learning paradigm, label distribution learning (LDL) explicitly models label ambiguity with the definition of label description degree. Although lots of work has been done to deal with real-world applications, theoretical results on LDL remain unexplored. In this paper, we rethink LDL from theoretical aspects, towards analyzing learnability of LDL. Firstly, risk bounds for three representative LDL algorithms (AA-kNN, AA-BP and SA-ME) are provided. For AA-kNN, Lipschitzness of the label distribution function is assumed to bound the risk, and for AA-BP and SA-ME, rademacher complexity
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Cohen, William W. "USING DISTRIBUTION-FREE LEARNING THEORY TO ANALYZE SOLUTION-PATH CACHING MECHANISMS." Computational Intelligence 8, no. 2 (1992): 336–75. http://dx.doi.org/10.1111/j.1467-8640.1992.tb00370.x.

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Juba, Brendan. "On learning finite-state quantum sources." Quantum Information and Computation 12, no. 1&2 (2012): 105–18. http://dx.doi.org/10.26421/qic12.1-2-7.

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We examine the complexity of learning the distributions produced by finite-state quantum sources. We show how prior techniques for learning hidden Markov models can be adapted to the {\em quantum generator} model to find that the analogous state of affairs holds: information-theoretically, a polynomial number of samples suffice to approximately identify the distribution, but computationally, the problem is as hard as learning parities with noise, a notorious open question in computational learning theory.
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Haghtalab, Nika, Matthew O. Jackson, and Ariel D. Procaccia. "Belief polarization in a complex world: A learning theory perspective." Proceedings of the National Academy of Sciences 118, no. 19 (2021): e2010144118. http://dx.doi.org/10.1073/pnas.2010144118.

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We present two models of how people form beliefs that are based on machine learning theory. We illustrate how these models give insight into observed human phenomena by showing how polarized beliefs can arise even when people are exposed to almost identical sources of information. In our first model, people form beliefs that are deterministic functions that best fit their past data (training sets). In that model, their inability to form probabilistic beliefs can lead people to have opposing views even if their data are drawn from distributions that only slightly disagree. In the second model,
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Caicedo, Santiago, Robert E. Lucas, and Esteban Rossi-Hansberg. "Learning, Career Paths, and the Distribution of Wages." American Economic Journal: Macroeconomics 11, no. 1 (2019): 49–88. http://dx.doi.org/10.1257/mac.20170390.

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We develop a theory of career paths and earnings where agents organize in production hierarchies. Agents climb these hierarchies as they learn stochastically from others. Earnings grow as agents acquire knowledge and occupy positions with more subordinates. We contrast these and other implications with US census data for the period 1990 to 2010, matching the Lorenz curve of earnings and the observed mean experience-earnings profiles. We show the increase in wage inequality over this period can be rationalized with a shift in the level of the complexity and profitability of technologies relativ
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Ghosh, Himadri, and Prajneshu. "Statistical learning theory for fitting multimodal distribution to rainfall data: an application." Journal of Applied Statistics 38, no. 11 (2011): 2533–45. http://dx.doi.org/10.1080/02664763.2011.559210.

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Powell, Nathan, and Andrew J. Kurdila. "Distribution-free learning theory for approximating submanifolds from reptile motion capture data." Computational Mechanics 68, no. 2 (2021): 337–56. http://dx.doi.org/10.1007/s00466-021-02034-0.

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Dissertations / Theses on the topic "Distribution learning theory"

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Słowiński, Witold. "Autonomous learning of domain models from probability distribution clusters." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=211059.

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Nontrivial domains can be difficult to understand and the task of encoding a model of such a domain can be difficult for a human expert, which is one of the fundamental problems of knowledge acquisition. Model learning provides a way to address this problem by allowing a predictive model of the domain's dynamics to be learnt algorithmically, without human supervision. Such models can provide insight about the domain to a human or aid in automated planning or reinforcement learning. This dissertation addresses the problem of how to learn a model of a continuous, dynamic domain, from sensory obs
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Liu, Jiaping. "A Study on Distribution Learning of Generative Adversarial Networks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41250.

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This thesis is an exploration of the properties of shallow generative adversarial networks (GANs). We focus on several aspects of GANs to investigate the learnability of a class of distributions using shallow GANs and conduct experiments to explore the influence of these aspects on the performance of the GAN models. We identify and analyze several pathological phenomena in theoretical analysis and experiments, and propose potential solutions for them.
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Curley, Tracy R. "Organizational Learning Theory and Districtwide Curriculum Reform: The Role of the Principal in Organizational Learning." Thesis, Boston College, 2016. http://hdl.handle.net/2345/bc-ir:106803.

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Thesis advisor: Rebecca Lownhaupt<br>This qualitative case study examined the role of the principal in organizational learning in one small, urban school district. The study focused on ways in which building leaders acquired, interpreted, and distributed information in schools, and how these practices were monitored. Findings from analysis of principal interviews and document review showed that monthly meetings with the superintendent served as the primary source of information gathered by principals, while meetings with their peers provided a vehicle for interpreting information shared. Withi
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Chapman, Anna. "An investigation into the distribution of leadership in extended learning activities through the lens of cultural historical activity theory." Thesis, University of East London, 2017. http://roar.uel.ac.uk/6854/.

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In order to address the criticism that Distributed Leadership (DL) literature is vague, confusing, has misleading definitions and is contradictory (Spillane and Coldren, 2011, p.26), this thesis puts forward a different approach in the form of a ‘Universal Leadership Culture’. This was developed from the findings of a study which aimed to investigate the distribution of leadership in Extended Learning Activities (ELAs), delivered in Centres placed in high profile sports clubs in England, through the particular Government initiative of ‘Playing for Success’ (PfS). Within an interpretative parad
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Li, Bin. "Statistical learning and predictive modeling in data mining." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155058111.

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Minsker, Stanislav. "Non-asymptotic bounds for prediction problems and density estimation." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44808.

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This dissertation investigates the learning scenarios where a high-dimensional parameter has to be estimated from a given sample of fixed size, often smaller than the dimension of the problem. The first part answers some open questions for the binary classification problem in the framework of active learning. Given a random couple (X,Y) with unknown distribution P, the goal of binary classification is to predict a label Y based on the observation X. Prediction rule is constructed from a sequence of observations sampled from P. The concept of active learning can be informally characterized as f
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Doran, Gary Brian Jr. "Multiple-Instance Learning from Distributions." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1417736923.

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Srivastava, Santosh. "Bayesian minimum expected risk estimation of distributions for statistical learning /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6765.

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Nilsson, Viktor. "Prediction of Dose Probability Distributions Using Mixture Density Networks." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273610.

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In recent years, machine learning has become utilized in external radiation therapy treatment planning. This involves automatic generation of treatment plans based on CT-scans and other spatial information such as the location of tumors and organs. The utility lies in relieving clinical staff from the labor of manually or semi-manually creating such plans. Rather than predicting a deterministic plan, there is great value in modeling it stochastically, i.e. predicting a probability distribution of dose from CT-scans and delineated biological structures. The stochasticity inherent in the RT trea
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Emerson, Guy Edward Toh. "Functional distributional semantics : learning linguistically informed representations from a precisely annotated corpus." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/284882.

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The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? The current state of the art is to represent meanings as vectors - but vectors do not correspond to any traditional notion of meaning. In particular, there is no way to talk about 'truth', a crucial concept in logic and formal semantics. In this thesis, I develop a framework for distributional semantics which answers this challenge. The meaning of a word
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Books on the topic "Distribution learning theory"

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Vapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. Springer New York, 1995.

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Vidyasagar, M. Learning and Generalisation: With Applications to Neural Networks. Springer London, 2003.

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Gabbay, Dov M. Abductive Reasoning and Learning. Springer Netherlands, 2000.

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Knowledge - Its Creation, Distribution and Economic Significance: Knowledge and Knowledge Production. Princeton University Press, 2016.

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Machlup, Fritz. Knowledge : Its Creation, Distribution and Economic Significance, Volume I: Knowledge and Knowledge Production. Princeton University Press, 2014.

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Machlup, Fritz. Knowledge : Its Creation, Distribution and Economic Significance, Volume I: Knowledge and Knowledge Production. Princeton University Press, 2014.

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Machlup, Fritz. Knowledge : Its Creation, Distribution and Economic Significance, Volume III: The Economics of Information and Human Capital. Princeton University Press, 2014.

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Machlup, Fritz. Knowledge: Its Creation, Distribution and Economic Significance. Princeton University Press, 2016.

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Machlup, Fritz. Knowledge: Its Creation, Distribution and Economic Significance. Princeton University Press, 2014.

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Sastry, Kumara, Martin Pelikan, and Erick Cantú-Paz. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer, 2010.

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Book chapters on the topic "Distribution learning theory"

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Dudík, Miroslav, and Robert E. Schapire. "Maximum Entropy Distribution Estimation with Generalized Regularization." In Learning Theory. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_12.

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Marchetti-Spaccamela, A., and M. Protasi. "Learning under uniform distribution." In Fundamentals of Computation Theory. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51498-8_32.

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Jackson, Jeffrey C. "Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention." In Learning Theory. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_24.

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Wang, Li-Fang, and Jian-Chao Zeng. "Estimation of Distribution Algorithm Based on Copula Theory." In Evolutionary Learning and Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12834-9_7.

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Jackson, Jeffrey C., and Rocco A. Servedio. "Learning Random Log-Depth Decision Trees under the Uniform Distribution." In Learning Theory and Kernel Machines. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45167-9_44.

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Hada, Takahiro, and Yuko Osana. "Reinforcement Learning by KFM Probabilistic Associative Memory Based on Weights Distribution and Area Neuron Increase and Decrease." In Neural Information Processing. Theory and Algorithms. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17537-4_50.

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Achlioptas, Dimitris, and Frank McSherry. "On Spectral Learning of Mixtures of Distributions." In Learning Theory. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_31.

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Caramanis, Constantine, and Shie Mannor. "An Inequality for Nearly Log-Concave Distributions with Applications to Learning." In Learning Theory. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27819-1_37.

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Freund, Yoav, Alon Orlitsky, Prasad Santhanam, and Junan Zhang. "Universal Coding of Zipf Distributions." In Learning Theory and Kernel Machines. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45167-9_57.

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Yoshinaka, Ryo. "Integration of the Dual Approaches in the Distributional Learning of Context-Free Grammars." In Language and Automata Theory and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28332-1_46.

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Conference papers on the topic "Distribution learning theory"

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Masiha, Mohammad Saeed, Amin Gohari, Mohammad Hossein Yassaee, and Mohammad Reza Aref. "Learning under Distribution Mismatch and Model Misspecification." In 2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021. http://dx.doi.org/10.1109/isit45174.2021.9517732.

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Liu, Hsiang-Chuan, Shih-Neng Wu, Hsien-Chang Tsai, and Yih-Chang Ou. "Polytomous ordering theory algorithm based on empirical distribution critical value." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016891.

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Lee, Junesuk, Geon-Tae Ahn, Byoung-Ju Yun, and Soon-Yong Park. "Slag Removal Path Estimation by Slag Distribution and Deep Learning." In 15th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008944602460252.

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Chen, Wei, Yan-Hong Guo, and Bao-Ting Li. "Profit distribution model of integrated logistics service provider based on principal-agent theory." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580520.

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Xu, Yu, and Zhi-Ming Chen. "Evaluation of power supply capability in medium voltage distribution networks based on fuzzy theory." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016671.

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Li, M., and P. M. B. Vitanyi. "A theory of learning simple concepts under simple distributions and average case complexity for the universal distribution." In 30th Annual Symposium on Foundations of Computer Science. IEEE, 1989. http://dx.doi.org/10.1109/sfcs.1989.63452.

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Yong-Xiu He, Wei Wang, Liang-Qi Wu, and Fu-Rong Li. "Assessment the connecting style of power distribution network based on fuzzy and blind number theory." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620491.

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Zhang, Teng, and Hai Jin. "Optimal Margin Distribution Machine for Multi-Instance Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/330.

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Multi-instance learning (MIL) is a celebrated learning framework where each example is represented as a bag of instances. An example is negative if it has no positive instances, and vice versa if at least one positive instance is contained. During the past decades, various MIL algorithms have been proposed, among which the large margin based methods is a very popular class. Recently, the studies on margin theory disclose that the margin distribution is of more importance to generalization ability than the minimal margin. Inspired by this observation, we propose the multi-instance optimal margi
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Rostami, Mohammad, Soheil Kolouri, and Praveen K. Pilly. "Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/463.

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Despite huge success, deep networks are unable to learn effectively in sequential multitask learning settings as they forget the past learned tasks after learning new tasks. Inspired from complementary learning systems theory, we address this challenge by learning a generative model that couples the current task to the past learned tasks through a discriminative embedding space. We learn an abstract generative distribution in the embedding that allows generation of data points to represent past experience. We sample from this distribution and utilize experience replay to avoid forgetting and s
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Ling, Dan, Hong-Zhong Huang, Qiang Miao, and Bo Yang. "Parameter Estimation for Weibull Distribution Using Support Vector Regression." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34617.

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The Weibull distribution is widely used in life testing and reliability studies. Weibull analysis is the process of discovering the trends in product or system failure data, and using them to predict future failures in similar situations. Support Vector Regression is a machine learning method based on statistical learning theory, which has been applied successfully to solve forecasting problems in many fields. In this paper, support vector regression is used to build a parameter estimating model for Weibull distribution. Numerical examples are presented to show good performance of this method.
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Reports on the topic "Distribution learning theory"

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Robledo, Ana, and Amber Gove. What Works in Early Reading Materials. RTI Press, 2019. http://dx.doi.org/10.3768/rtipress.2018.op.0058.1902.

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Access to books is key to learning to read and sustaining a love of reading. Yet many low- and middle-income countries struggle to provide their students with reading materials of sufficient quality and quantity. Since 2008, RTI International has provided technical assistance in early reading assessment and instruction to ministries of education in dozens of low- and middle-income countries. The central objective of many of these programs has been to improve learning outcomes—in particular, reading—for students in the early grades of primary school. Under these programs, RTI has partnered with
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Lichand, Guilherme, Carlos Alberto Dória, Onicio Leal Neto, and João Cossi. The Impacts of Remote Learning in Secondary Education: Evidence from Brazil during the Pandemic. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003344.

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The goal of this paper is to document the pedagogic impacts of the remote learning strategy used by an state department of education in Brazil during the pandemic. We found that dropout risk increased by 365% under remote learning. While risk increased with local disease activity, most of it can be attributed directly to the absence of in-person classes: we estimate that dropout risk increased by no less than 247% across the State, even at the low end of the distribution of per capita Covid-19 cases. Average standardized test scores decreased by 0.32 standard deviation, as if students had only
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Dubeck, Margaret M., Jonathan M. B. Stern, and Rehemah Nabacwa. Learning to Read in a Local Language in Uganda: Creating Learner Profiles to Track Progress and Guide Instruction Using Early Grade Reading Assessment Results. RTI Press, 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0068.2106.

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The Early Grade Reading Assessment (EGRA) is used to evaluate studies and monitor projects that address reading skills in low- and middle-income countries. Results are often described solely in terms of a passage-reading subtask, thereby overlooking progress in related skills. Using archival data of cohort samples from Uganda at two time points in three languages (Ganda, Lango, and Runyankore-Rukiga), we explored a methodology that uses passage-reading results to create five learner profiles: Nonreader, Beginner, Instructional, Fluent, and Next-Level Ready. We compared learner profiles with re
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