Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing
Authors: Sepanta Zeighami, Cyrus Shahabi
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments showing these asymptotic bounds are achieved in practice. ... 5. Experiments We empirically validate our theoretical results on synthetic and real datasets (specified in each experiment). |
| Researcher Affiliation | Academia | 1Univerisity of Southern California. |
| Pseudocode | Yes | Algorithm 1 PCA Index Construction... Algorithm 2 Recursive Distribution Search Algorithm... Algorithm 3 RDA Index Construction... Algorithm 4 RDA Index Querying... |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code available or a link to a code repository. |
| Open Datasets | Yes | We use 6 real datasets commonly used for benchmarking learned indexes. ... The datasets are WL and IOT from Ferragina & Vinciguerra (2020); Kraska et al. (2018); Galakatos et al. (2019) and BK, FB, OSM, WK from Marcus et al. (2020) described next. |
| Dataset Splits | No | The paper states: "For each real dataset, we sample n data points uniformly at random for different values of n from the original dataset, and queries are generated uniformly at random from the data range." It does not provide explicit training, validation, or test set split percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or other libraries). |
| Experiment Setup | Yes | For each experiment, we report index size and number of operations. ... We present results for ϵ = 0.1 and ϵ = 0.01, where ϵ is the space complexity parameter in Theorem 3.1. ... for multiple values of ρ and ρ1 or ρ2 to see at what values the trends predicted by the theorems emerge. |