Search results for "PAC-learning"

Hits ?▲	Authors	Title	Venue	Year	Link	Author keywords
81	Kayvan Najarian	A Fixed-Distribution PAC Learning Theory for Neural FIR Models.	J. Intell. Inf. Syst.	2005	DBLP DOI BibTeX RDF	nonlinear FIR model, multi-layer feedforward neural networks, m-dependency, PAC learning
71	François Denis	PAC Learning from Positive Statistical Queries.	ALT	1998	DBLP DOI BibTeX RDF
61	Sanjeev R. Kulkarni, Sanjoy K. Mitter, John N. Tsitsiklis, Ofer Zeitouni	PAC Learning with Generalized Samples and an Applicaiton to Stochastic Geometry.	IEEE Trans. Pattern Anal. Mach. Intell.	1993	DBLP DOI BibTeX RDF	probably approximately correct learning, generalized samples, sample size bounds, signal processing, signal processing, stochastic processes, geometry, learning systems, PAC learning, stochastic geometry, curve reconstruction, geometric reconstruction
61	Nicolò Cesa-Bianchi, Eli Dichterman, Paul Fischer, Eli Shamir 0001, Hans Ulrich Simon	Sample-Efficient Strategies for Learning in the Presence of Noise.	J. ACM	1999	DBLP DOI BibTeX RDF	learning with malicious noise, PAC learning
60	Stephen H. Muggleton	From ILP to PILP.	SBIA	2008	DBLP DOI BibTeX RDF
58	Yasuhiro Tajima, Matsuaki Terada	A PAC Learnability of Simple Deterministic Languages.	ICGI	2002	DBLP DOI BibTeX RDF	learning via queries, simple deterministic language, representative sample, PAC learning
55	Stephen Kwek	Learning Intermediate Concepts.	ALT	2001	DBLP DOI BibTeX RDF	multiple concepts, mistake bound algorithm, PAC learning, membership queries, exact learning
54	Stephen H. Muggleton	Bayesian Inductive Logic Programming.	COLT	1994	DBLP DOI BibTeX RDF
52	Arturo Hernández Aguirre, Bill P. Buckles, Antonio Martínez-Alcántara	The probably approximately correct (PAC) population size of a genetic algorithm.	ICTAI	2000	DBLP DOI BibTeX RDF	probably approximately correct, PAC population size, approximately correct behavior, PAC framework, GA population, genetic algorithms, genetic algorithm, probability, learnability, PAC-learning, learning by example, learning machines
48	Rocco A. Servedio	Smooth Boosting and Learning with Malicious Noise.	COLT/EuroCOLT	2001	DBLP DOI BibTeX RDF
46	Kayvan Najarian, Guy Albert Dumont, Michael S. Davies, Nancy E. Heckman	Neural ARX Models and PAC Learning.	AI	2000	DBLP DOI BibTeX RDF
46	Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler	Scale-sensitive dimensions, uniform convergence, and learnability.	J. ACM	1997	DBLP DOI BibTeX RDF	uniform laws of large numbers, PAC learning, Vapnik-Chervonenkis dimension
45	Vitaly Feldman	Hardness of approximate two-level logic minimization and PAC learning with membership queries.	STOC	2006	DBLP DOI BibTeX RDF	DNF minimization, proper learning, two-level logic minimization, hardness of approximation, uniform distribution, membership queries, truth table
42	Adam R. Klivans, Amir Shpilka	Learning Arithmetic Circuits via Partial Derivatives.	COLT	2003	DBLP DOI BibTeX RDF	learning with queries, PAC learning
42	Adam R. Klivans, Alexander A. Sherstov	Cryptographic Hardness for Learning Intersections of Halfspaces.	FOCS	2006	DBLP DOI BibTeX RDF
42	Paul W. Goldberg	When Can Two Unsupervised Learners Achieve PAC Separation?	COLT/EuroCOLT	2001	DBLP DOI BibTeX RDF
40	Jorge Castro, Ricard Gavaldà	Towards Feasible PAC-Learning of Probabilistic Deterministic Finite Automata.	ICGI	2008	DBLP DOI BibTeX RDF
40	Luc De Raedt	PAC-Learning Logic Programs under the Closed-World Assumption.	ISMIS	1996	DBLP DOI BibTeX RDF
39	Javed A. Aslam, Scott E. Decatur	General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Bounding	FOCS	1993	DBLP DOI BibTeX RDF	statistical query learning, hypothesis boosting, complexity, noise, PAC learning, general bounds
39	Sanjeev R. Kulkarni, John N. Tsitsiklis, Sanjoy K. Mitter, Ofer Zeitouni	PAC Learning With Generalized Samples and an Application to Stochastic Geometry.	COLT	1992	DBLP DOI BibTeX RDF
39	Nader H. Bshouty, Lynn Burroughs	On the Proper Learning of Axis Parallel Concepts.	COLT	2002	DBLP DOI BibTeX RDF
38	Aditi Dhagat, Lisa Hellerstein	PAC Learning with Irrelevant Attributes	FOCS	1994	DBLP DOI BibTeX RDF	polynomial-time Occam algorithm, irrelevant attributes, Occam algorithm, decision lists, greedy set cover, polynomial-time, PAC learning
37	Roni Khardon, Rocco A. Servedio	Maximum Margin Algorithms with Boolean Kernels.	COLT	2003	DBLP DOI BibTeX RDF
36	Ariel D. Procaccia, Jeffrey S. Rosenschein	Learning to identify winning coalitions in the PAC model.	AAMAS	2006	DBLP DOI BibTeX RDF	coalition formation, PAC learning
36	Guofei Jiang, George Cybenko	Functional Validation in Grid Computing.	Auton. Agents Multi Agent Syst.	2004	DBLP DOI BibTeX RDF	keywords and ontology, grid computing, PAC learning, service matching, functional validation
36	Jeffrey C. Jackson, Christino Tamon, Tomoyuki Yamakami	Quantum DNF Learnability Revisited.	COCOON	2002	DBLP DOI BibTeX RDF
35	Ming Li 0001, Paul M. B. Vitányi	Computational Machine Learning in Theory and Praxis.	Computer Science Today	1995	DBLP DOI BibTeX RDF
35	Vitaly Feldman	Evolvability from learning algorithms.	STOC	2008	DBLP DOI BibTeX RDF	evolvability, pac learning, statistical query
32	Jyh-Han Lin, Jeffrey Scott Vitter	A Theory for Memory-Based Learning.	COLT	1992	DBLP DOI BibTeX RDF
32	Michael J. Kearns, Robert E. Schapire, Linda Sellie	Toward Efficient Agnostic Learning.	COLT	1992	DBLP DOI BibTeX RDF
31	Hae Yong Kim	Binary halftone image resolution increasing by decision tree learning.	IEEE Trans. Image Process.	2004	DBLP DOI BibTeX RDF
29	Rocco A. Servedio	On PAC Learning Algorithms for Rich Boolean Function Classes.	TAMC	2006	DBLP DOI BibTeX RDF
29	François Denis, Cyrille D'Halluin, Rémi Gilleron	PAC Learning with Simple Examples.	STACS	1996	DBLP DOI BibTeX RDF
29	Wolfgang Maass 0001	Efficient Agnostic PAC-Learning with Simple Hypothesis.	COLT	1994	DBLP DOI BibTeX RDF
29	Hans Ulrich Simon	Smart PAC-Learners.	ALT	2009	DBLP DOI BibTeX RDF
29	Rocco A. Servedio	On Learning Embedded Midbit Functions.	ALT	2002	DBLP DOI BibTeX RDF
28	Yasuhiro Tajima, Yoshiyuki Kotani, Matsuaki Terada	An Analysis of Examples and a Search Space for PAC Learning of Simple Deterministic Languages with Membership Queries.	ICGI	2004	DBLP DOI BibTeX RDF
27	Rüdiger Reischuk, Thomas Zeugmann	A Complete and Tight Average-Case Analysis of Learning Monomials.	STACS	1999	DBLP DOI BibTeX RDF
26	Einoshin Suzuki	Worst Case and a Distribution-Based Case Analyses of Sampling for Rule Discovery Based on Generality and Accuracy.	Appl. Intell.	2005	DBLP DOI BibTeX RDF	knowledge discovery in data bases, sampling, rule discovery
26	Ke Yang 0005	On Learning Correlated Boolean Functions Using Statistical Queries.	ALT	2001	DBLP DOI BibTeX RDF
25	Bruno Apolloni, Fabio Baraghini, Giorgio Palmas	PAC Meditation on Boolean Formulas.	SARA	2002	DBLP DOI BibTeX RDF
25	Einoshin Suzuki	Worst-Case Analysis of Rule Discovery.	Discovery Science	2001	DBLP DOI BibTeX RDF
25	Arturo Hernández Aguirre, Bill P. Buckles, Carlos A. Coello Coello	On Learning kDNFsn Boolean Formulas.	Evolvable Hardware	2001	DBLP DOI BibTeX RDF
23	Bruno Apolloni, Andrea Brega, Dario Malchiodi, Giorgio Palmas, Anna Maria Zanaboni	Learning Rule Representations from Boolean Data.	ICANN	2003	DBLP DOI BibTeX RDF
23	Shirley H. C. Cheung	Learning from Approximate Data.	COCOON	2000	DBLP DOI BibTeX RDF
23	Dennis Cheung	Learning Real Polynomials with a Turing Machine.	ALT	1999	DBLP DOI BibTeX RDF
23	Nader H. Bshouty, Sally A. Goldman, H. David Mathias, Subhash Suri, Hisao Tamaki	Noise-Tolerant Distribution-Free Learning of General Geometric Concepts.	J. ACM	1998	DBLP DOI BibTeX RDF	geometric concepts, computational learning
23	Mary Cryan, Leslie Ann Goldberg, Paul W. Goldberg	Evolutionary Trees can be Learned in Polynomial Time in the Two-State General Markov Model.	FOCS	1998	DBLP DOI BibTeX RDF	PAC Learnability, Algorithms, evolutionary trees, discrete distributions
23	Paul W. Goldberg, Sally A. Goldman	Learning One-Dimensional Geometric Patterns Under One-Sided Random Misclassification Noise.	COLT	1994	DBLP DOI BibTeX RDF
23	Shan-Hwei Nienhuys-Cheng, Mark Polman	Sample PAC-Learnability in Model Inference.	ECML	1994	DBLP DOI BibTeX RDF
23	Ryan O'Donnell, Rocco A. Servedio	The chow parameters problem.	STOC	2008	DBLP DOI BibTeX RDF	chow parameters, boolean function, fourier analysis, threshold function
23	Thomas Zeugmann	Stochastic Finite Learning.	SAGA	2001	DBLP DOI BibTeX RDF	stochastic finite learning, conjunctive concepts, pattern languages, average-case analysis, Inductive inference
23	Leslie G. Valiant	Evolvability.	J. ACM	2009	DBLP DOI BibTeX RDF	SQ learning, Evolvable, PAC learning
23	Alexander A. Sherstov	The Intersection of Two Halfspaces Has High Threshold Degree.	FOCS	2009	DBLP DOI BibTeX RDF	intersections of halfspaces, polynomial representations of Boolean functions, PAC learning, rational approximation, direct product theorems
23	Yasuhiro Tajima, Yoshiyuki Kotani	Polynomial Time Probabilistic Learning of a Subclass of Linear Languages with Queries.	ICGI	2008	DBLP DOI BibTeX RDF	learning via queries, linear language, representative sample, PAC learning
23	Adam R. Klivans, Alexander A. Sherstov	Unconditional lower bounds for learning intersections of halfspaces.	Mach. Learn.	2007	DBLP DOI BibTeX RDF	Intersections of halfspaces, Halfspace learning, SQ learning, Lower bounds for learning, Harmonic sieve, PAC learning, Query learning, Statistical queries, Polynomial threshold functions
23	Marco Muselli, Francesca Ruffino	Reliable Learning: A Theoretical Framework.	KES (3)	2007	DBLP DOI BibTeX RDF	reliable learning, generalization, error bounds, PAC learning, loss function
23	Russell Greiner, Xiaoyuan Su, Bin Shen, Wei Zhou	Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers.	Mach. Learn.	2005	DBLP DOI BibTeX RDF	(Bayesian) belief nets, computational/sample complexity, classification, logistic regression, PAC-learning
23	Elchanan Mossel, Sébastien Roch	Learning nonsingular phylogenies and hidden Markov models.	STOC	2005	DBLP DOI BibTeX RDF	hidden Markov models, PAC learning, evolutionary trees, phylogenetic reconstruction
23	Leslie G. Valiant	A neuroidal architecture for cognitive computation.	J. ACM	2000	DBLP DOI BibTeX RDF	learning relations, robust reasoning, nonmonotonic reasoning, PAC learning, computational learning, cognitive computation
23	Hanzhong Gu, Haruhisa Takahashi	How Bad May Learning Curves Be?.	IEEE Trans. Pattern Anal. Mach. Intell.	2000	DBLP DOI BibTeX RDF	worst-case learning, interpolation dimension, Generalization, concept learning, PAC learning, generalization error, learning curves, sample complexity
23	Jun Tarui, Tatsuie Tsukiji	Learning DNF by Approximating Inclusion-Exclusion Formulae.	CCC	1999	DBLP DOI BibTeX RDF	Inclusion-exclusion formula, PAC Learning, DNF
23	Peter Auer, Philip M. Long, Aravind Srinivasan	Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets.	STOC	1997	DBLP DOI BibTeX RDF	approximations of distributions, machine learning, random graphs, derandomization, pseudorandomness, PAC learning, multiple-instance learning, rectangles, sample complexity, explicit constructions, Ramsey graphs
23	Nader H. Bshouty, Zhixiang Chen 0001, Steven Homer	On Learning Discretized Geometric Concepts (Extended Abstract)	FOCS	1994	DBLP DOI BibTeX RDF	finite injury priority method, online learning algorithm, geometric concept, discretized geometric concepts, classes of rectangles, polynomial time, PAC-learning, equivalence queries, halfspaces
23	Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler	Scale-sensitive Dimensions, Uniform Convergence, and Learnability	FOCS	1993	DBLP DOI BibTeX RDF	probabilistic concepts, scale-sensitive dimensions, PAC learning model, distribution-free convergence property, uniform Gliveako-Cantelli classes, statistical regression framework, learnability, uniform convergence
23	Jörg-Uwe Kietz	Some Lower Bounds for the Computational Complexity of Inductive Logic Programming.	ECML	1993	DBLP DOI BibTeX RDF	Inductive Logic Programming, PAC-Learning
21	Akihiro Yamamoto, Kouichi Hirata	Workshop on Learning with Logics and Logics for Learning (LLLL).	JSAI Workshops	2005	DBLP DOI BibTeX RDF
21	Masako Sato, Yasuhito Mukouchi, Mikiharu Terada	Refutable/Inductive Learning from Neighbor Examples and Its Application to Decision Trees over Patterns.	Progress in Discovery Science	2002	DBLP DOI BibTeX RDF
18	Maria-Florina Balcan, Avrim Blum, Santosh S. Vempala	A discriminative framework for clustering via similarity functions.	STOC	2008	DBLP DOI BibTeX RDF	clustering, learning, similarity functions
18	Guillaume Stempfel, Liva Ralaivola	Learning Kernel Perceptrons on Noisy Data Using Random Projections.	ALT	2007	DBLP DOI BibTeX RDF
18	Nader H. Bshouty, Lynn Burroughs	Bounds for the Minimum Disagreement Problem with Applications to Learning Theory.	COLT	2002	DBLP DOI BibTeX RDF
18	Marcus Schaefer 0001	Deciding the K-Dimension is PSPACE-Complete.	CCC	2000	DBLP DOI BibTeX RDF	computational complexity, learning theory, PSPACE
18	Anthony D. Griffiths, Derek G. Bridge	On Concept Space and Hypothesis Space in Case-Based Learning Algorithms.	ECML	1995	DBLP DOI BibTeX RDF
18	Yasubumi Sakakibara, Rani Siromoney	A Noise Model on Learning Sets of Strings.	COLT	1992	DBLP DOI BibTeX RDF
17	Colin de la Higuera, Jean-Christophe Janodet, Frédéric Tantini	Learning Languages from Bounded Resources: The Case of the DFA and the Balls of Strings.	ICGI	2008	DBLP DOI BibTeX RDF	Polynomial learnability, balls of strings, edit distance, deterministic finite automata
17	Jon Feldman, Ryan O'Donnell, Rocco A. Servedio	Learning mixtures of product distributions over discrete domains.	FOCS	2005	DBLP DOI BibTeX RDF
17	Ken Satoh	Learning Taxonomic Relation by Case-Based Reasoning.	ALT	2000	DBLP DOI BibTeX RDF
17	Stephen H. Muggleton	Inductive Logic Programming: Derivations, Successes and Shortcomings.	ECML	1993	DBLP DOI BibTeX RDF
17	Mark Bun, Aloni Cohen, Rathin Desai	Private PAC Learning May be Harder than Online Learning.	CoRR	2024	DBLP DOI BibTeX RDF
17	Mark Bun, Aloni Cohen, Rathin Desai	Private PAC Learning May be Harder than Online Learning.	ALT	2024	DBLP BibTeX RDF
17	Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas	Optimal Learners for Realizable Regression: PAC Learning and Online Learning.	CoRR	2023	DBLP DOI BibTeX RDF
17	Lucas Gretta, Eric Price 0001	An Improved Online Reduction from PAC Learning to Mistake-Bounded Learning.	SOSA	2023	DBLP DOI BibTeX RDF
17	Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas	Optimal Learners for Realizable Regression: PAC Learning and Online Learning.	NeurIPS	2023	DBLP BibTeX RDF
17	Omar Montasser, Steve Hanneke, Nathan Srebro	Reducing Adversarially Robust Learning to Non-Robust PAC Learning.	CoRR	2020	DBLP BibTeX RDF
17	Omar Montasser, Steve Hanneke, Nati Srebro	Reducing Adversarially Robust Learning to Non-Robust PAC Learning.	NeurIPS	2020	DBLP BibTeX RDF
17	Pirkko Kuusela, Daniel Ocone	Learning with side information: PAC learning bounds.	J. Comput. Syst. Sci.	2004	DBLP DOI BibTeX RDF
17	Javed A. Aslam, Scott E. Decatur	General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting.	Inf. Comput.	1998	DBLP DOI BibTeX RDF
17	Manuel Blum 0001	Program Error Detection/Correction: Turning PAC Learning into PERFECT Learning (Abstract).	ALT	1997	DBLP DOI BibTeX RDF
17	Eli Shamir 0001, Clara Shwartzman	Learning by extended statistical queries and its relation to PAC learning.	EuroCOLT	1995	DBLP DOI BibTeX RDF
17	Eiji Takimoto, Akira Maruoka	Relationships between learning and information compression based on PAC learning model.	Syst. Comput. Jpn.	1993	DBLP DOI BibTeX RDF
17	Naoki Abe	On the Sample Complexity of Various Learning Strategies in the Probabilistic PAC Learning Paradigms.	Nonmonotonic and Inductive Logic	1991	DBLP DOI BibTeX RDF
15	Jinyan Su, Jinhui Xu, Di Wang 0015	PAC learning halfspaces in non-interactive local differential privacy model with public unlabeled data.	J. Comput. Syst. Sci.	2024	DBLP DOI BibTeX RDF
15	Leonardo Nagami Coregliano, Maryanthe Malliaris	High-arity PAC learning via exchangeability.	CoRR	2024	DBLP DOI BibTeX RDF
15	Ashwin Nayak 0001, Pulkit Sinha	Proper vs Improper Quantum PAC learning.	CoRR	2024	DBLP DOI BibTeX RDF
15	Ari Karchmer	Distributional PAC-Learning from Nisan's Natural Proofs.	ITCS	2024	DBLP DOI BibTeX RDF
15	Mateo Perez, Fabio Somenzi, Ashutosh Trivedi 0001	A PAC Learning Algorithm for LTL and Omega-Regular Objectives in MDPs.	AAAI	2024	DBLP DOI BibTeX RDF
15	Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao	PAC-learning for Strategic Classification.	J. Mach. Learn. Res.	2023	DBLP BibTeX RDF
15	Andrew James Turner, Ata Kabán	PAC-learning with approximate predictors.	Mach. Learn.	2023	DBLP DOI BibTeX RDF
15	Tom Gur, Wilfred Salmon, Sergii Strelchuk	Provable Advantage in Quantum PAC Learning.	Electron. Colloquium Comput. Complex.	2023	DBLP BibTeX RDF
15	Ari Karchmer	Average-Case PAC-Learning from Nisan's Natural Proofs.	Electron. Colloquium Comput. Complex.	2023	DBLP BibTeX RDF