A theory of the learnable

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

Cited by (1,004)

Items 601–700 of 1,004

  1. Learnability and Automatizability
    Michael Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi · 2004 · Annual Symposium on Foundations of Computer Science
  2. Approximating Hyper-Rectangles: Learning and Pseudorandom Sets
    Peter L. Auer, Philip M. Long, Aravind Srinivasan · 1998 · Journal of Computer and System Sciences
  3. Surrogates for numerical simulations; optimization of eddy-promoter heat exchangers
    Serhat Yeşilyurt, Anthony T. Patera · 1995 · Computer Methods in Applied Mechanics and Engineering
  4. Exact learning of read-twice DNF formulas
    Howard Jay Aizenstein, Leonard Pitt · 2002 · Annual Symposium on Foundations of Computer Science
  5. A computational model of teaching
    Jeffrey C. Jackson, Andrew Tomkins · 1992 · Conference on Learning Theory
  6. Simulation-Based Functional Test Generation for Embedded Processors
    Charles H.‐P. Wen, Li-C. Wang, Kwang-Ting Tim Cheng · 2006 · IEEE Transactions on Computers
  7. Bayesian Rule Sets for Interpretable Classification
    Tong Wang, Cynthia D Rudin, Finale Doshi‐Velez, Yimin Liu, Erica Klampfl, Perry MacNeille · 2016 · Proceedings
  8. Data‐Driven Shape Analysis and Processing
    Kai Xu, Vladimir G. Kim, Qi-xing Huang A, Evangelos Kalogerakis · 2016 · Computer Graphics Forum
  9. As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI
    Federico Cabitza, Andrea Campagner, Luca Maria Sconfienza · 2020 · BMC Medical Informatics and Decision Making
  10. Logic-Based Explainability in Machine Learning
    João P. Marques-Silva · 2023 · Lecture notes in computer science
  11. The complexity of properly learning simple concept classes
    Misha Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi · 2007 · Journal of Computer and System Sciences
  12. Finite time analysis of the pursuit algorithm for learning automata
    Rajaraman Kanagasabai, P. SESHADRI SASTRY · 1996 · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
  13. Associative Reinforcement Learning: Functions in k-DNF
    Leslie Pack Kaelbling · 1994 · Machine Learning
  14. Applying Boosting Techniques to Genetic Programming
    Grégory Paris, Denis Robilliard, Cyril Fonlupt · 2002 · Lecture notes in computer science
  15. Learning one-counter languages in polynomial time
    Piotr Berman, Robert S. Roos · 1987 · Annual Symposium on Foundations of Computer Science
  16. On the Complexity of Teaching
    Sally A. Goldman, Michael J. Kearns · 1991 · Elsevier eBooks
  17. Statistical Relational Learning With Unconventional String Models
    Mai H. Vu, Ashkan Zehfroosh, Kristina Strother‐Garcia, Michael A. Sebok, Jeffrey Nicholas Heinz, Herbert G. Tanner · 2018 · Frontiers in Robotics and AI
  18. Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices
    Ranjit Kumar Paul, Sandip Garai · 2021 · Soft Computing
  19. The Sample Complexity of Learning Fixed-Structure Bayesian Networks
    Sanjoy Dasgupta · 1997 · Machine Learning
  20. A Computational Learning Theory of Active Object Recognition Under Uncertainty
    Alexander Andreopoulos, John K. Tsotsos · 2012 · International Journal of Computer Vision
  21. Sample Compression Schemes for VC Classes
    Shay Moran, Amir Yehudayoff · 2016 · Journal of the ACM
  22. Learning Weighted Automata
    Borja Balle, Mehryar Mohri · 2015 · Lecture notes in computer science
  23. Simpler PAC-Bayesian bounds for hostile data
    Pierre Alquier, Benjamin Guedj · 2017 · Machine Learning
  24. E-generalization using grammars
    Jochen Burghardt · 2005 · Artificial Intelligence
  25. A tamper-proof and lightweight authentication scheme
    Ghaith Hammouri, Erdinç Öztürk, Berk Sunar · 2008 · Pervasive and Mobile Computing
  26. Unconditional lower bounds for learning intersections of halfspaces
    Adam R. Klivans, Alexander A. Sherstov · 2007 · Machine Learning
  27. On learning simple neural concepts: from halfspace intersections to neural decision lists
    Mario Marchand, Mostefa Golea · 1993 · Network Computation in Neural Systems
  28. Applying Data Mining to Pseudo-Relevance Feedback for High Performance Text Retrieval
    Xiangji Huang, Yan Huang, Miao Wen, Aijun An, Yang Liu, Josiah Poon · 2006 · Proceedings
  29. Convergent Learning Algorithms for Unknown Reward Games
    Archie C. Chapman, David S. Leslie, Alex Rogers, Nicholas R. Jennings · 2013 · SIAM Journal on Control and Optimization
  30. Ironing in the Dark
    Tim Roughgarden, Okke Schrijvers · 2016 · ACM Conference on Economics and Computation (EC)
  31. Learning minimal abstractions
    Percy Liang, Omer Tripp, Mayur Naik · 2011 · ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL)
  32. Tree Adjoining Grammars, Language Bias, and Genetic Programming
    Nguyễn Xuân Hoài, Bob McKay, Hussein A. Abbass · 2003 · Lecture notes in computer science
  33. Polynomial regression under arbitrary product distributions
    Eric Blais, Ryan O’Donnell, Karl Wimmer · 2010 · Machine Learning
  34. Performance and efficiency: recent advances in supervised learning
    Sheng Ma, Chuanyi Ji · 1999 · Proceedings of the IEEE
  35. New degree bounds for polynomial threshold functions
    Ryan O’Donnell, Rocco A. Servedio · 2010 · COMBINATORICA
  36. A Survey of Reinforcement Learning from Human Feedback
    Kaufmann, Timo, Weng, Paul, Bengs, Viktor, Hüllermeier, Eyke · 2023 · arXiv
  37. The great number crunch1
    Charles D. Yang · 2008 · Journal of Linguistics
  38. Experimental Quantum Advantage with Quantum Coupon Collector
    Min-Gang Zhou, Xiaoyu Cao, Yu-Shuo Lu, Yang Wang, Yu Bao, Zhao-Ying Jia, Yao Fu, Hua‐Lei Yin, Zeng‐Bing Chen · 2022 · Research
  39. On the Expressibility and Overfitting of Quantum Circuit Learning
    Chih-Chieh Chen, Masaya Watabe, Kodai Shiba, Masaru Sogabe, Katsuyoshi Sakamoto, Tomah Sogabe · 2021 · ACM Transactions on Quantum Computing
  40. Testing by means of inductive program learning
    Francesco Bergadano, Daniele Gunetti · 1996 · ACM Transactions on Software Engineering and Methodology
  41. Accurate and efficient processor performance prediction via regression tree based modeling
    Бин Ли, Lu Peng, Balachandran Ramadass · 2009 · Journal of Systems Architecture
  42. Boosting by weighting critical and erroneous samples
    Vanessa Gómez-Verdejo, Manuel Ortega-Moral, Jerónimo Arenas‐García, Aníbal Ramón Figueiras-Vidal · 2006 · Neurocomputing
  43. Maximizing the Margin with Boosting
    Gunnar Rätsch, Manfred K. Warmuth · 2002 · Lecture notes in computer science
  44. A System for Building Intelligent Agents that Learn to Retrieve and Extract Information
    Tina Eliassi‐Rad, JUDE W. SHAVLIK · 2003 · User Modeling and User-Adapted Interaction
  45. Learning Function-Free Horn Expressions
    Roni Khardon · 1999 · Machine Learning
  46. On Polynomial-Time Learnability in the Limit of Strictly Deterministic Automata
    Takashi Yokomori · 1995 · Machine Learning
  47. Paradigms of truth detection
    Daniel N. Osherson, Scott A. Weinstein · 1989 · Journal of Philosophical Logic
  48. Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries
    Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, Thomas Zeugmann · 2001 · Theoretical Computer Science
  49. Sample Complexity Bounds on Differentially Private Learning via Communication Complexity
    Vitaly Feldman, David Xiao · 2015 · SIAM Journal on Computing
  50. Logical analysis of data: classification with justification
    Endre Boros, Yves Crama, Peter L. Hammer, Toshihide Ibaraki, Alexander Kogan, Kazuhisa Makino · 2011 · Annals of Operations Research
  51. Forecasting third-party mobile payments with implications for customer flow prediction
    Shaohui Ma, Robert A. Fildes · 2020 · International Journal of Forecasting
  52. On capturing human skills and knowledge; algorithmic approaches to model identification
    William B. Rouse, John M. Hammer, C. Michael Lewis · 1989 · IEEE Transactions on Systems Man and Cybernetics
  53. Learning and Smoothed Analysis
    Adam Tauman Kalai, Alex Samorodnitsky, Shang‐Hua Teng · 2009 · Annual Symposium on Foundations of Computer Science
  54. Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions
    Sally A. Goldman, Michael J. Kearns, Robert E. Schapire · 1993 · SIAM Journal on Computing
  55. On the applications of multiplicity automata in learning
    Amos Beimel, Francesco Bergadano, Nader H. Bshouty, Eyal Kushilevitz Z, Stefano Varricchio · 2002 · Annual Symposium on Foundations of Computer Science
  56. Information Extraction in Structured Documents Using Tree Automata Induction
    Raymond Kosala, Jan Van den Bussche, Maurice Bruynooghe, Hendrik Blockeel · 2002 · Lecture notes in computer science
  57. On Learning Ring-Sum-Expansions
    Paul Fischer, Hans Ulrich Simon · 1992 · SIAM Journal on Computing
  58. Regression Trees and Rule-Based Models
    Max Kühn, Kjell Johnson · 2013 · Springer eBooks
  59. QUALIFIED PREDICTIONS FOR MICROARRAY AND PROTEOMICS PATTERN DIAGNOSTICS WITH CONFIDENCE MACHINES
    Anthony Graham Bellotti, Zhiyuan Luo, Alex Gammerman, Frederik W. van Delft, Vaskar Saha · 2005 · International Journal of Neural Systems
  60. The limitations of optimization from samples
    Eric Balkanski, Aviad Rubinstein, Yaron Singer · 2017 · ACM Symposium on Theory of Computing (STOC)
  61. Safe but not Overcautious Motion Planning under Occlusions and Limited Sensor Range
    Maximilian Naumann, Hendrik Königshof, Martin Lauer, Christoph Stiller · 2019 · IEEE Intelligent Vehicles Symposium
  62. Bloom Filters in Adversarial Environments
    Moni Naor, Eylon Yogev · 2015 · Lecture notes in computer science
  63. Towards Learning in CARIN-ALN
    Céline Rouveirol, Véronique Ventos · 2000 · Lecture notes in computer science
  64. Artificial intelligence implementation in the APS process diagnostic
    Sofiane Guessasma, Zahir Salhi, Ghislain Montavon, Patrick Gougeon, Christian Coddet · 2004 · Materials Science and Engineering B
  65. A Real generalization of discrete AdaBoost
    Richard Nock, Frank Nielsen · 2006 · Artificial Intelligence
  66. Learning in the framework of fuzzy lattices
    V. Petridis, Vassilis G. Kaburlasos · 1999 · IEEE Transactions on Fuzzy Systems
  67. Inductive inference of unbounded unions of pattern languages from positive data
    Takeshi Shinohara, Hiroki Arimura · 2000 · Theoretical Computer Science
  68. On exact specification by examples
    Martin Anthony, Graham Brightwell, Dave Cohen, John S. Shawe-Taylor · 1992 · Conference on Learning Theory
  69. What size network is good for generalization of a specific task of interest?
    Bagrat R. Amirikian, Hajime Nishimura · 1994 · Neural Networks
  70. Generalization and PAC learning: some new results for the class of generalized single-layer networks
    Sean B. Holden, P.J.W. Rayner · 1995 · IEEE Transactions on Neural Networks
  71. A Polynomial-time Algorithm for Learning k-variable Pattern Languages from Examples
    Michael J. Kearns, Leonard Pitt · 1989 · Elsevier eBooks
  72. Coping with Uncertainty in Map Learning
    Kenneth Basye, Thomas Dean, Jeffrey Scott Vitter · 1997 · Machine Learning
  73. Learning Regular Languages over Large Alphabets
    Oded Maler, Irini-Eleftheria Mens · 2014 · Lecture notes in computer science
  74. Synthesis of optimal switching logic for hybrid systems
    Susmit Jha, Sanjit Arunkumar Seshia, Ashish Tiwari · 2011 · ACM International Conference on Embedded Software (EMSOFT)
  75. Recommendation Systems: A Probabilistic Analysis
    Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins · 2001 · Journal of Computer and System Sciences
  76. Partial observability and learnability
    Loizos Michael · 2010 · Artificial Intelligence
  77. Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts
    Wolfgang Maass, György Turán · 1994 · Machine Learning
  78. New degree bounds for polynomial threshold functions
    Ryan O’Donnell, Rocco A. Servedio · 2003 · ACM Symposium on Theory of Computing (STOC)
  79. A survey of grammatical inference in software engineering
    Andrew Stevenson, James R. Cordy · 2014 · Science of Computer Programming
  80. Predicting dengue importation into Europe, using machine learning and model-agnostic methods
    Donald Salami, Carla Alexandra Sousa, Maria do Rosário Oliveira Martins, César Capinha · 2020 · Scientific Reports
  81. Collegial activity learning between heterogeneous sensors
    Kyle Feuz, Diane J. Cook · 2017 · Knowledge and Information Systems
  82. Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain
    Thomas Hannagan, Jonathan Grainger B · 2012 · Cognitive Science
  83. The Power of Self-Directed Learning
    Sally A. Goldman, Robert H. Sloan · 1994 · Machine Learning
  84. On Basing Lower-Bounds for Learning on Worst-Case Assumptions
    Benny Applebaum, Boaz Barak, David Xiao · 2008 · Annual Symposium on Foundations of Computer Science
  85. Toward a Unified Science of Machine Learning
    Pat Langley · 1989 · Machine Learning
  86. On the Concept of Software Obfuscation in Computer Security
    Nikolay Kuzurin, Alexander Vladimirovich Shokurov, Николай Павлович Варновский, Vladimir Anatolyevich Zakharov · 2007 · Lecture notes in computer science
  87. Distributed Decomposed Data Analytics in Fog Enabled IoT Deployments
    Mohit Taneja, Nikita Jalodia, Alan Davy · 2019 · IEEE Access
  88. The Adversarial Robustness of Sampling
    Omri Ben‐Eliezer, Eylon Yogev · 2020 · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS)
  89. On the Robustness of Domain Constraints
    Ryan Sheatsley, Blaine Hoak, Eric Pauley, Yohan Beugin, Michael J. Weisman, Patrick Drew McDaniel · 2021 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  90. Learning robots interacting with humans: from epistemic risk to responsibility
    Matteo Santoro, Dante Marino, Guglielmo Tamburrini · 2007 · AI & Society
  91. A PAC-Bayesian margin bound for linear classifiers
    Ralf Herbrich, Thore Graepel · 2002 · IEEE Transactions on Information Theory
  92. Machine Learning and Knowledge Extraction in Digital Pathology Needs an Integrative Approach
    Andreas Holzinger, Bernd Malle, Peter Kieseberg, Peter M. Roth, Heimo Müller, Robert Reihs, Kurt Zatloukal · 2017 · Lecture notes in computer science
  93. Cellular automata in pattern recognition
    Raghu Raghavan · 1993 · Information Sciences
  94. PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples
    Philip M. Long, Lei Tan · 1998 · Machine Learning
  95. A Framework for Average Case Analysis of Conjunctive Learning Algorithms
    Michael J. Pazzani, Wendy Sarrett · 1992 · Machine Learning
  96. Model Uncertainty in Discrete Event Systems
    Stanley D. Young, Vijay K. Garg · 1995 · SIAM Journal on Control and Optimization
  97. Analyzing and repairing concept drift adaptation in data stream classification
    Ben Halstead, Yun Sing Koh, Patricia Riddle, Russel Pears, Mykola Pechenizkiy, Albert Bifet, Gustavo Olivares, Guy Coulson · 2021 · Machine Learning
  98. Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets
    Yann Chevaleyre, Frédéric Koriche, Jérôme Lang, Jérôme Mengin, Bruno Zanuttini · 2010 · Springer eBooks
  99. Learning Economic Parameters from Revealed Preferences
    Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani · 2014 · Lecture notes in computer science
  100. Learning schema mappings
    Balder ten Cate, Víctor Dalmau, Phokion G. Kolaitis · 2013 · ACM Transactions on Database Systems