On the public citation graph
The Computational Complexity of Densest Region Detection
0 signed · 23 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known work and none has been retracted — and get a dated CiteStamped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/10.1006/jcss.1997.1507openalex-snapshot
Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher
inferredcites →https://doi.org/10.1007/978-1-4419-5821-1_19openalex-snapshot
Universal Donsker Classes and Metric Entropy
inferredcites →https://doi.org/10.1007/bf02187916openalex-snapshot
On the complexity of polyhedral separability
inferredcites →https://doi.org/10.1016/0022-1236(82)90093-3openalex-snapshot
On a characterization of operators from lq into a Banach space of type p with some applications to eigenvalue problems
inferredcites →https://doi.org/10.1016/0304-3975(78)90006-3openalex-snapshot
The densest hemisphere problem
inferredcites →https://doi.org/10.1016/s0022-0000(03)00038-2openalex-snapshot
On the difficulty of approximately maximizing agreements
inferredcites →https://doi.org/10.1016/s1571-0661(04)00301-9openalex-snapshot
Parameterized Complexity
inferredcites →https://doi.org/10.1145/258533.258536openalex-snapshot
Some optimal inapproximability results
inferredcites →https://doi.org/10.1214/aop/1176991978openalex-snapshot
Universal Donsker Classes and Metric Entropy
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_22openalex-snapshot
Alternative Measures of Computational Complexity with Applications to Agnostic Learning
inferred← citeshttps://doi.org/10.1007/3-540-44581-1_19openalex-snapshot
On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses
inferred← citeshttps://doi.org/10.1007/978-3-030-01258-8_43openalex-snapshot
Robust Fitting in Computer Vision: Easy or Hard?
inferred← citeshttps://doi.org/10.1007/s11263-019-01207-yopenalex-snapshot
Robust Fitting in Computer Vision: Easy or Hard?
inferred← citeshttps://doi.org/10.1016/j.compind.2012.10.005openalex-snapshot
Data quality evaluation and improvement for prognostic modeling using visual assessment based data partitioning method
inferred← citeshttps://doi.org/10.1016/s0022-0000(03)00038-2openalex-snapshot
On the difficulty of approximately maximizing agreements
inferred← citeshttps://doi.org/10.1109/tpami.2016.2631531openalex-snapshot
Efficient Globally Optimal Consensus Maximisation with Tree Search
inferred← citeshttps://doi.org/10.1109/tpami.2017.2663859openalex-snapshot
Guest Editorial: Best of CVPR 2015
inferred← citeshttps://doi.org/10.1145/1824777.1824783openalex-snapshot
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
inferred← citeshttps://doi.org/10.1287/moor.2023.1355openalex-snapshot
A Geometric Model of Opinion Polarization
inferred← citeshttps://doi.org/10.2200/s00757ed1v01y201702cov011openalex-snapshot
The Maximum Consensus Problem: Recent Algorithmic Advances
inferred← citeshttps://doi.org/10.24963/ijcai.2020/23openalex-snapshot
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
inferred← citeshttps://doi.org/10.48550/arxiv.0805.2891openalex-snapshot
Learning Low-Density Separators
inferred← citeshttps://doi.org/10.48550/arxiv.2011.01956openalex-snapshot
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
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.