A theory of the learnable

Valiant, L. G.·1984·Communications of the ACM

Cited by (1,004)

Items 101–200 of 1,004

  1. Principles of metareasoning
    Stuart Russell, Eric H. Wefald · 1991 · Artificial Intelligence
  2. On the complexity of differentially private data release
    Cynthia Dwork, Moni Naor, Omer Reingold, Guy N. Rothblum, Salil Vadhan · 2009 · ACM Symposium on Theory of Computing (STOC)
  3. Transfer Learning for Drug Discovery
    Chenjing Cai, Shiwei Wang, Youjun Xu, Weilin Zhang, Ke Tang, Qi Ouyang, Luhua Lai, Jianfeng Pei · 2020 · Journal of Medicinal Chemistry
  4. Efficient progressive sampling
    Foster John Provost, David D. Jensen, Tim Oates · 1999 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  5. Shadow Tomography of Quantum States
    Scott Aaronson · 2018 · SIAM Journal on Computing
  6. Number-theoretic constructions of efficient pseudo-random functions
    Moni Naor, Omer Reingold · 2002 · Annual Symposium on Foundations of Computer Science
  7. Toward efficient agnostic learning
    Michael J. Kearns, Robert E. Schapire, Linda M. Sellie · 1992 · Conference on Learning Theory
  8. An information theoretic approach to rule induction from databases
    Padhraic Smyth, R.M. Goodman · 1992 · IEEE Transactions on Knowledge and Data Engineering
  9. What Can We Learn Privately?
    Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim B, Sofya Raskhodnikova, Adam Smith · 2008 · Annual Symposium on Foundations of Computer Science
  10. A Survey of Recent Trends in One Class Classification
    Shehroz S. Khan, Michael G. Madden · 2010 · Lecture notes in computer science
  11. 50 Years of object recognition: Directions forward
    Andreopoulos, Alexander, Tsotsos, John K. · 2013 · Computer Vision and Image Understanding
  12. Digital medicine and the curse of dimensionality
    Berisha, Visar, Krantsevich, Chelsea, Hahn, P. Richard, Hahn, Shira, Dasarathy, Gautam, Turaga, Pavan, Liss, Julie · 2021 · npj Digital Medicine
  13. Computational Mechanics: Pattern and Prediction, Structure and Simplicity
    Shalizi, Cosma Rohilla, Crutchfield, James P. · 2001 · Journal of Statistical Physics
  14. Learning in the Presence of Malicious Errors
    Michael J. Kearns, Ming Li · 1993 · SIAM Journal on Computing
  15. A Conservation Law for Generalization Performance
    Cullen Schaffer · 1994 · Elsevier eBooks
  16. Cryptographic Primitives Based on Hard Learning Problems
    Avrim L. Blum, Merrick Furst, Michael J. Kearns, Richard J. Lipton · 1994 · Lecture notes in computer science
  17. A survey on data‐efficient algorithms in big data era
    Adadi, Amina · 2021 · Journal of Big Data
  18. THE EVOLVING CONTEXT OF THE FUNDAMENTAL DIFFERENCE HYPOTHESIS
    Bley-Vroman, Robert · 2009 · Studies in Second Language Acquisition
  19. Learning Decision Trees Using the Fourier Spectrum
    Eyal Kushilevitz Z, Yishay Mansour · 1993 · SIAM Journal on Computing
  20. Collaborative recommendation
    Michael P. O’Mahony, Neil Hurley, Nicholas Kushmerick, Guenole C. M. Silvestre · 2004 · ACM Transactions on Internet Technology
  21. Symbolic and Neural Learning Algorithms: An Experimental Comparison
    JUDE W. SHAVLIK, Raymond J. Mooney, Geoffrey G. Towell · 1991 · Machine Learning
  22. Chernoff–Hoeffding Bounds for Applications with Limited Independence
    Jeanette P. Schmidt, Alan R. Siegel, Aravind Srinivasan · 1995 · SIAM Journal on Discrete Mathematics
  23. Neural Networks for Pattern Recognition
    Suraj C. Kothari, Heekuck Oh · 1993 · Advances in computers
  24. An algorithmic theory of learning: Robust concepts and random projection
    Rosa I. Arriaga, Santosh Vempala · 2006 · Machine Learning
  25. Boosting Algorithms: A Review of Methods, Theory, and Applications
    Artur J. Ferreira, Mário A. T. Figueiredo · 2012 · Springer eBooks
  26. Using Sampling and Queries to Extract Rules from Trained Neural Networks
    Mark W. Craven, JUDE W. SHAVLIK · 1994 · Elsevier eBooks
  27. Overfitting Avoidance as Bias
    Schaffer, Cullen · 1993 · Machine Learning
  28. Self-Directed Learning
    Todd Matthew Gureckis, Douglas Benjamin Markant · 2012 · Perspectives on Psychological Science
  29. Approaches to the Automatic Discovery of Patterns in Biosequences
    Alvis Brāzma, Inge Jonassen, Ingvar Eidhammer, David Gilbert · 1998 · Journal of Computational Biology
  30. The child's trigger experience: Degree-0 learnability
    DAVID W. LIGHTFOOT · 1989 · Behavioral and Brain Sciences
  31. Knowledge compilation and theory approximation
    Bart Selman, Henry Kautz · 1996 · Journal of the ACM
  32. Computing with Spiking Neuron Networks
    Hélène Paugam‐Moisy, Sander M. Bohté · 2012 · Springer eBooks
  33. Predicting short-term stock prices using ensemble methods and online data sources
    Bin Weng, Lin Lü, Xing Wang, Fadel M. Megahed, Waldyn G. Martinez · 2018 · Expert Systems with Applications
  34. Combustion machine learning: Principles, progress and prospects
    Matthias Ihme, Wai Tong Chung, Aashwin Mishra · 2022 · Progress in Energy and Combustion Science
  35. A Survey of Methods for Scaling Up Inductive Algorithms
    Foster John Provost, Venkateswarlu Kolluri · 1999 · Data Mining and Knowledge Discovery
  36. Distributed Learning in Wireless Sensor Networks
    Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor · 2007 · Wiley eBooks
  37. Neural Network Exploration Using Optimal Experiment Design
    David A. Cohn · 1996 · Neural Networks
  38. Design and evolution of modular neural network architectures
    Bart L.M. Happel, Jacob M.J. Murre · 1994 · Neural Networks
  39. The strength of weak learnability
    Robert E. Schapire · 1989 · Annual Symposium on Foundations of Computer Science
  40. Settling the Polynomial Learnability of Mixtures of Gaussians
    Ankur Moitra, Gregory J. Valiant · 2010 · Annual Symposium on Foundations of Computer Science
  41. Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications
    J. Galindo, Pablo Tamayo · 2000 · Computational Economics
  42. Information-Theoretic Bounds on Quantum Advantage in Machine Learning
    Hsin-Yuan Huang, Richard Kueng, John P. Preskill · 2021 · Physical Review Letters
  43. Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension
    Sally Floyd, Manfred K. Warmuth · 1995 · Machine Learning
  44. Statistical Relational Artificial Intelligence
    Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole · 2016 · Synthesis lectures on artificial intelligence and machine learning
  45. A statistical approach to learning and generalization in layered neural networks
    Esther Levin, Naftali Tishby, Sara A. Solla · 1990 · Proceedings of the IEEE
  46. Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
    Alexandre A. Varnek, Igor I. Baskin · 2012 · Journal of Chemical Information and Modeling
  47. Constructing boosting algorithms from SVMs: an application to one-class classification
    Gunnar Rätsch, S. Mika, Bernhard Schölkopf, K. Robert Müller · 2002 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  48. Pseudorandomness
    Salil Vadhan · 2012 · Foundations and Trends® in Theoretical Computer Science
  49. Logical analysis of numerical data
    Endre Boros, Peter L. Hammer, Toshihide Ibaraki, Alexander Kogan · 1997 · Mathematical Programming
  50. Graph Neural Networks for Wireless Communications: From Theory to Practice
    Yifei Shen, Jun Zhang, Shenghui Song, Khaled B. Letaief · 2022 · IEEE Transactions on Wireless Communications
  51. Approximate inference of functional dependencies from relations
    Jyrki Kivinen, Heikki Mannila · 1995 · Theoretical Computer Science
  52. The growth of language: Universal Grammar, experience, and principles of computation
    Charles D Yang, Stephen Crain, Robert C. Berwick, Noam Chomsky, Johan J. Bolhuis · 2017 · Neuroscience & Biobehavioral Reviews
  53. Cause-effect relationships and partially defined Boolean functions
    Yves Crama, Peter L. Hammer, Toshihide Ibaraki · 1988 · Annals of Operations Research
  54. Learning from hints in neural networks
    Yaser S. Abu‐Mostafa · 1990 · Journal of Complexity
  55. Strategic Classification
    Moritz Hardt, Nimrod Megiddo, Christos H. Papadimitriou, Mary K. Wootters · 2016 · Conference on Innovations in Theoretical Computer Science
  56. PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
    Matthias Seeger · 2003 · Journal of Machine Learning Research
  57. Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression
    Durga Lal Shrestha, DIMITRI P. SOLOMATINE · 2006 · Neural Computation
  58. Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues
    Igino Corona, Giorgio Giacinto, Fabio Roli · 2013 · Information Sciences
  59. Negative Results for Equivalence Queries
    Dana Angluin · 1990 · Machine Learning
  60. An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution
    Jeffrey C. Jackson · 1997 · Journal of Computer and System Sciences
  61. Towards an evolutionary theory of language
    Martin A. Nowak, Natalia L. Komarova · 2001 · Trends in Cognitive Sciences
  62. Fuzzy neural networks and neurocomputations
    Witold Pedrycz · 1993 · Fuzzy Sets and Systems
  63. Learning from positive and unlabeled examples
    François Denis, Rémi Gilleron, Fabien Letouzey · 2005 · Theoretical Computer Science
  64. Detection of Interactive Stepping Stones: Algorithms and Confidence Bounds
    Avrim L. Blum, Dawn Song, Shobha Venkataraman · 2004 · Lecture notes in computer science
  65. Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review
    Jahanzaib Latif, Chuangbai Xiao, Azhar Imran, Shanshan Tu · 2019 · International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
  66. Fuzzy methods in machine learning and data mining: Status and prospects
    Eyke Hüllermeier · 2005 · Fuzzy Sets and Systems
  67. Quantum speed-up for unsupervised learning
    Esma Aı̈meur, Gilles Brassard, Sébastien Gambs · 2012 · Machine Learning
  68. Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement
    José Hernández‐Orallo · 2016 · Artificial Intelligence Review
  69. Human model evaluation in interactive supervised learning
    Fiebrink, Rebecca, Cook, Perry R., Trueman, Dan · 2011 · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  70. Conformal prediction: A unified review of theory and new challenges
    Matteo Fontana, Gianluca Zeni, Simone Vantini · 2022 · Bernoulli
  71. Learning from positive data
    Stephen Muggleton · 1997 · Lecture notes in computer science
  72. Defining artificial intelligence for librarians
    Andrew Martin Cox, Suvodeep Mazumdar A · 2022 · Journal of Librarianship and Information Science
  73. Minimax Estimation of Functionals of Discrete Distributions
    Jiantao Jiao, Kartik Venkat, Yanjun Han, Tsachy Weissman · 2015 · IEEE Transactions on Information Theory
  74. Agnostically Learning Halfspaces
    Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, Rocco A. Servedio · 2008 · SIAM Journal on Computing
  75. Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning
    Tao Zhang, Quanyan Zhu · 2016 · IEEE Transactions on Information Forensics and Security
  76. Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey
    P. R. Kumar, Atul Kumar Gupta · 2020 · Journal of Computer Science and Technology
  77. The learnability of quantum states
    Scott Aaronson · 2007 · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  78. Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network
    Ziyi Liu, Junfeng Gao, Guoguo Yang, Huan Zhang, Yong He · 2016 · Scientific Reports
  79. Learning read-once formulas with queries
    Dana Angluin, Lisa Hellerstein, Marek Karpiński · 1993 · Journal of the ACM
  80. Learning Conjunctions of Horn Clauses
    Dana Angluin, Michael Frazier, Leonard Pitt · 1992 · Machine Learning
  81. A Survey of On-Device Machine Learning
    Sauptik Dhar, Junyao Guo, Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah · 2021 · ACM Transactions on Internet of Things
  82. Self-training: A survey
    Massih-Reza Amini, Vasilii Feofanov, Loïc Pauletto, Liès Hadjadj, Émilie Devijver, Yury Maximov · 2024 · Neurocomputing
  83. On Issues of Instance Selection
    Liu, Huan, Motoda, Hiroshi · 2002 · Data Mining and Knowledge Discovery
  84. A Lockdown Technique to Prevent Machine Learning on PUFs for Lightweight Authentication
    Meng-Day Yu, Matthias Hiller, Jeroen Delvaux, Richard Sowell, Srinivas Devadas, Ingrid M.R. Verbauwhede · 2016 · IEEE Transactions on Multi-Scale Computing Systems
  85. Kolmogorov Complexity and its Applications
    Ming Li, Paul M. B. Vitanyi · 1990 · Elsevier eBooks
  86. Learning Categories From Few Examples With Multi Model Knowledge Transfer
    Tatiana Tommasi, Francesco Orabona, Barbara Caputo B · 2013 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  87. Boosting Methods for Regression
    Nigel Duffy, David P. Helmbold · 2002 · Machine Learning
  88. Inference in the age of big data: Future perspectives on neuroscience
    Danilo Bzdok, B.T. Thomas Yeo · 2017 · NeuroImage
  89. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
    Nick Littlestone · 1987 · Annual Symposium on Foundations of Computer Science
  90. Learning DNF in time 2Õ(n1/3)
    Adam R. Klivans, Rocco A. Servedio · 2003 · Journal of Computer and System Sciences
  91. Regression conformal prediction with random forests
    Ulf Johansson, Henrik Boström, Tuve Löfström, Henrik Linusson · 2014 · Machine Learning
  92. Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
    Muggleton, Stephen H., Lin, Dianhuan, Tamaddoni-Nezhad, Alireza · 2015 · Machine Learning
  93. Towards intelligent image retrieval
    John P. Eakins · 2002 · Pattern Recognition
  94. Theory of Disagreement-Based Active Learning
    Steve Hanneke · 2014 · Foundations and Trends® in Machine Learning
  95. PAC Learning from Positive Statistical Queries
    FranÇois Denis · 1998 · Lecture notes in computer science
  96. Programming by Demonstration Using Version Space Algebra
    Tessa A. Lau, Steven A. Wolfman, Pedro Domingos, Daniel S. Weld · 2003 · Machine Learning
  97. Efficient learning of context-free grammars from positive structural examples
    Yasubumi Sakakibara · 1992 · Information and Computation
  98. Learning intersections and thresholds of halfspaces
    Adam R. Klivans, Ryan O’Donnell, Rocco A. Servedio · 2004 · Journal of Computer and System Sciences
  99. Algorithmic and Analysis Techniques in Property Testing
    Dana Ron · 2010 · Foundations and Trends® in Theoretical Computer Science
  100. What connectionist models learn: Learning and representation in connectionist networks
    Stephen José Hanson, David J. Burr · 1990 · Behavioral and Brain Sciences