High Dimensional Clustering with r-nets
Authors: Georgia Avarikioti, Alain Ryser, Yuyi Wang, Roger Wattenhofer3207-3214
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we lay theoretical groundwork, by showing improved algorithms on the approximate r-net problem and applications thereof. This paper presents new theoretical results on the construction of (1 + ϵ)-approximate r-nets, improving the previous upper bound of O(dn2 Θ( ϵ)) by (Avarikioti et al. 2017). |
| Researcher Affiliation | Academia | Georgia Avarikioti, Alain Ryser, Yuyi Wang, Roger Wattenhofer ETH Zurich, Switzerland {zetavar,aryser,yuwang,wattenhofer}@ethz.ch |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The algorithms are described in prose within the text. |
| Open Source Code | No | The paper refers to the full version on arXiv: "The omitted proofs can be found in the full version of the paper (https://arxiv.org/abs/1811.02288)". This link is for the paper itself, not for source code. No other statement about code availability is present. |
| Open Datasets | No | The paper does not provide concrete access information for a publicly available or open dataset. It is a theoretical paper and does not describe experiments run on specific datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or predefined splits). It is a theoretical paper and does not describe empirical experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. The paper focuses on theoretical runtime complexities. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). It mentions techniques like 'Probabilistic Polynomial Threshold Functions' and 'Locality Sensitive Hashing' but not specific software implementations with versions. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings). It is a theoretical paper and does not describe empirical experiments requiring such setup details. |