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Learning Monotone Decision Trees in Polynomial Time

https://doi.org/10.1109/ccc.2006.25
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1006/inco.1994.1024openalex-snapshot
On Learning Monotone DNF Formulae under Uniform Distributions
inferredcites →https://doi.org/10.1006/inco.1995.1164openalex-snapshot
Exact Learning Boolean Functions via the Monotone Theory
inferredcites →https://doi.org/10.1006/jcss.1995.1043openalex-snapshot
An O(nlog log n) Learning Algorithm for DNF under the Uniform Distribution
inferredcites →https://doi.org/10.1006/jcss.1997.1533openalex-snapshot
An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution
inferredcites →https://doi.org/10.1007/3-540-49730-7_27openalex-snapshot
Learning Sub-classes of Monotone DNF on the Uniform Distribution
inferredcites →https://doi.org/10.1007/978-1-4615-2696-4_11openalex-snapshot
Learning Boolean Functions via the Fourier Transform
inferredcites →https://doi.org/10.1007/978-3-0348-8069-5_11openalex-snapshot
On a Nonsymmetric Version of the Khinchine-Kahane Inequality
inferredcites →https://doi.org/10.1007/pl00009809openalex-snapshot
Boolean Functions With Low Average Sensitivity Depend On Few Coordinates
inferredcites →https://doi.org/10.1007/s000370050005openalex-snapshot
On P versus NP $ \cap $ co-NP for decision trees and read-once branching programs
inferredcites →https://doi.org/10.1007/s002249910002openalex-snapshot
Learning Monotone Log-Term DNF Formulas under the Uniform Distribution
inferredcites →https://doi.org/10.1016/0020-0190(87)90114-1openalex-snapshot
Occam's Razor
inferredcites →https://doi.org/10.1016/0196-6774(84)90016-6openalex-snapshot
Short monotone formulae for the majority function
inferredcites →https://doi.org/10.1016/b978-1-55860-146-8.50027-8openalex-snapshot
LEARNING DNF UNDER THE UNIFORM DISTRIBUTION IN QUASI-POLYNOMIAL TIME
inferredcites →https://doi.org/10.1016/b978-1-55860-213-7.50032-8openalex-snapshot
Improved Learning of AC0 Functions
inferredcites →https://doi.org/10.1016/j.ic.2004.04.003openalex-snapshot
On learning monotone DNF under product distributions
inferredcites →https://doi.org/10.1016/s0020-0190(98)00165-3openalex-snapshot
Learning conjunctions with noise under product distributions
inferredcites →https://doi.org/10.1090/s0002-9939-96-03732-xopenalex-snapshot
Every monotone graph property has a sharp threshold
inferredcites →https://doi.org/10.1109/sfcs.1988.21923openalex-snapshot
The influence of variables on Boolean functions
inferredcites →https://doi.org/10.1109/sfcs.1993.366857openalex-snapshot
Exact learning via the Monotone theory
inferredcites →https://doi.org/10.1109/sfcs.1998.743491openalex-snapshot
On learning monotone Boolean functions
inferredcites →https://doi.org/10.1109/sfcs.2005.34openalex-snapshot
Every decision tree has an in.uential variable
inferredcites →https://doi.org/10.1145/130385.130391openalex-snapshot
An O(n log log n ) learning algorithm for DNF under the uniform distribution
inferredcites →https://doi.org/10.1145/167088.167197openalex-snapshot
Cryptographic hardness of distribution-specific learning
inferredcites →https://doi.org/10.1145/174130.174138openalex-snapshot
Constant depth circuits, Fourier transform, and learnability
inferredcites →https://doi.org/10.1145/195058.195147openalex-snapshot
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
inferredcites →https://doi.org/10.1145/195613.195656openalex-snapshot
Learning Boolean formulas
inferredcites →https://doi.org/10.1145/1968.1972openalex-snapshot
A theory of the learnable
inferredcites →https://doi.org/10.1145/225058.225125openalex-snapshot
On the Fourier spectrum of monotone functions
inferredcites →https://doi.org/10.1145/3749740.3749746openalex-snapshot
A Theory of the Learnable
inferredcites →https://doi.org/10.1145/509907.510018openalex-snapshot
Learnability beyond AC 0
inferredcites →https://doi.org/10.1145/6490.6503openalex-snapshot
How to construct random functions
inferredcites →https://doi.org/10.1184/r1/6605354openalex-snapshot
Every decision tree has an influential variable
inferredcites →https://doi.org/10.5555/92571.92659openalex-snapshot
Learning DNF under the uniform distribution in quasi-polynomial time
inferredcites →https://doi.org/10.7551/mitpress/3897.001.0001openalex-snapshot
An Introduction to Computational Learning Theory

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/11750321_42openalex-snapshot
On PAC Learning Algorithms for Rich Boolean Function Classes
inferred← citeshttps://doi.org/10.1007/978-0-387-30162-4_195openalex-snapshot
Learning Constant-Depth Circuits
inferred← citeshttps://doi.org/10.1007/978-1-4939-2864-4_195openalex-snapshot
Learning Constant-Depth Circuits
inferred← citeshttps://doi.org/10.1007/978-3-540-72927-3_28openalex-snapshot
Learning Nested Halfspaces and Uphill Decision Trees
inferred← citeshttps://doi.org/10.1007/978-3-540-74958-5_17openalex-snapshot
The Cost of Learning Directed Cuts
inferred← citeshttps://doi.org/10.1007/978-3-540-85363-3_38openalex-snapshot
Learning Random Monotone DNF
inferred← citeshttps://doi.org/10.1007/978-3-642-27848-8_195-2openalex-snapshot
Learning Constant-Depth Circuits (1993; Linial, Mansour, Nisan)
inferred← citeshttps://doi.org/10.1007/978-981-95-0215-8_15openalex-snapshot
Average-Case Deterministic Query Complexity of Boolean Functions with Fixed Weight
inferred← citeshttps://doi.org/10.1007/s00440-022-01176-3openalex-snapshot
Upper bounds on the one-arm exponent for dependent percolation models
inferred← citeshttps://doi.org/10.1016/j.tcs.2007.05.018openalex-snapshot
On PAC learning algorithms for rich Boolean function classes
inferred← citeshttps://doi.org/10.1080/10586458.2009.10128886openalex-snapshot
Discrete Morse Functions from Fourier Transforms
inferred← citeshttps://doi.org/10.1145/3564246.3585117openalex-snapshot
Testing Distributional Assumptions of Learning Algorithms
inferred← citeshttps://doi.org/10.1214/10-aop582openalex-snapshot
Oded Schramm’s contributions to noise sensitivity
inferred← citeshttps://doi.org/10.1214/11-aop659openalex-snapshot
On the scaling limits of planar percolation
inferred← citeshttps://doi.org/10.48550/arxiv.2005.00566openalex-snapshot
Relationships between the number of inputs and other complexity measures of Boolean functions
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