On the public citation graph
On Sample-Based Testers
0 signed · 50 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/rsa.10078openalex-snapshot
Three theorems regarding testing graph properties
inferredcites →https://doi.org/10.1006/jcss.1999.1656openalex-snapshot
Testing Problems with Sublearning Sample Complexity
inferredcites →https://doi.org/10.1007/978-3-540-48413-4_10openalex-snapshot
Improved Testing Algorithms for Monotonicity
inferredcites →https://doi.org/10.1007/978-3-642-16367-8openalex-snapshot
Property testing: current research and surveys
inferredcites →https://doi.org/10.1007/978-3-642-22935-0_49openalex-snapshot
Proximity Oblivious Testing and the Role of Invariances
inferredcites →https://doi.org/10.1007/978-3-642-32512-0_48openalex-snapshot
Two-Sided Error Proximity Oblivious Testing
inferredcites →https://doi.org/10.1007/s00037-016-0136-9openalex-snapshot
Non-interactive proofs of proximity
inferredcites →https://doi.org/10.1007/s00453-001-0078-7openalex-snapshot
Property Testing in Bounded Degree Graphs
inferredcites →https://doi.org/10.1016/0022-0000(93)90044-wopenalex-snapshot
Self-testing/correcting with applications to numerical problems
inferredcites →https://doi.org/10.1109/sfcs.2000.892113openalex-snapshot
Testing that distributions are close
inferredcites →https://doi.org/10.1109/sfcs.2003.1238182openalex-snapshot
On the Implementation of Huge Random Objects
inferredcites →https://doi.org/10.1137/s0097539700366528openalex-snapshot
Regular Languages are Testable with a Constant Number of Queries
inferredcites →https://doi.org/10.1137/s0097539793255151openalex-snapshot
Robust Characterizations of Polynomials with Applications to Program Testing
inferredcites →https://doi.org/10.1137/s0895480101407444openalex-snapshot
Testing Basic Boolean Formulae
inferredcites →https://doi.org/10.1145/2554797.2554841openalex-snapshot
Partial tests, universal tests and decomposability
inferredcites →https://doi.org/10.1145/285055.285060openalex-snapshot
Property testing and its connection to learning and approximation
inferredcites →https://doi.org/10.1145/335305.335315openalex-snapshot
On the efficiency of local decoding procedures for error-correcting codes
inferredcites →https://doi.org/10.1145/509907.509977openalex-snapshot
Monotonicity testing over general poset domains
inferredcites →https://doi.org/10.1214/aos/1176346255openalex-snapshot
The Equivalence of Weak, Strong and Complete Convergence in $L_1$ for Kernel Density Estimates
inferredcites →https://doi.org/10.13182/nse68-a19548openalex-snapshot
The Art of Computer Programming
inferredcites →https://doi.org/10.1561/0400000029openalex-snapshot
Algorithmic and Analysis Techniques in Property Testing
inferredcites →https://doi.org/10.1561/2200000004openalex-snapshot
Property Testing: A Learning Theory Perspective
inferredcites →https://doi.org/10.48550/arxiv.1306.1360openalex-snapshot
Some properties are not even partially testable
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/10.1002/rsa.20797openalex-snapshot
Testing convexity of figures under the uniform distribution
inferred← citeshttps://doi.org/10.1007/978-3-031-88946-2_11openalex-snapshot
On the Lower Bound on the Length of Relaxed Locally Decodable Codes
inferred← citeshttps://doi.org/10.1109/tit.2024.3483995openalex-snapshot
Exploring the Gap Between Tolerant and Non-Tolerant Distribution Testing
inferred← citeshttps://doi.org/10.1137/1.9781611976465.100openalex-snapshot
A Structural Theorem for Local Algorithms with Applications to Coding, Testing, and Privacy
inferred← citeshttps://doi.org/10.1137/16m1075661openalex-snapshot
Erasure-Resilient Property Testing
inferred← citeshttps://doi.org/10.1137/21m1422781openalex-snapshot
A Structural Theorem for Local Algorithms with Applications to Coding, Testing, and Verification
inferred← citeshttps://doi.org/10.1145/3155296openalex-snapshot
Parameterized Property Testing of Functions
inferred← citeshttps://doi.org/10.1145/3313276.3316302openalex-snapshot
Testing graphs in vertex-distribution-free models
inferred← citeshttps://doi.org/10.1145/3406325.3451104openalex-snapshot
VC dimension and distribution-free sample-based testing
inferred← citeshttps://doi.org/10.1145/3512750openalex-snapshot
Optimal Distribution-Free Sample-Based Testing of Subsequence-Freeness with One-Sided Error
inferred← citeshttps://doi.org/10.1145/3717823.3718134openalex-snapshot
Testing Support Size More Efficiently Than Learning Histograms
inferred← citeshttps://doi.org/10.4230/lipics.approx/random.2022.27openalex-snapshot
Exploring the Gap Between Tolerant and Non-Tolerant Distribution Testing
inferred← citeshttps://doi.org/10.4230/lipics.icalp.2022.71openalex-snapshot
Downsampling for Testing and Learning in Product Distributions
inferred← citeshttps://doi.org/10.4230/lipics.itcs.2019.52openalex-snapshot
Lower Bounds for Tolerant Junta and Unateness Testing via Rejection Sampling of Graphs
inferred← citeshttps://doi.org/10.48550/arxiv.1706.09362openalex-snapshot
Sample-based high-dimensional convexity testing
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.