Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Approximation Guarantees for Adaptive Sampling
Authors: Eric Balkanski, Yaron Singer
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,730 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,642 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we conduct experiments on real data sets in which the curvature and homogeneity properties can be easily manipulated and demonstrate the relationship between approximation and curvature, as well as the effectiveness of adaptive sampling in practice. |
| Researcher Affiliation | Academia | 1Harvard University. Correspondence to: Eric Balkanski <EMAIL>, Yaron Singer <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 ADAPTIVE-SAMPLING |
| Open Source Code | No | The paper mentions using external tools like "python package fancyimpute (Rubinsteyn & Feldman, 2017)" but does not state that the code for the authors' methodology is open-source or provide a link to it. |
| Open Datasets | Yes | We use the Movie Lens 1M dataset (Harper & Konstan., 2015)... We use 2 millions taxi trips in June 2017 from the New York City taxi and limousine commission trip record dataset (NYCTaxi-Limousine-Commission, 2017)... |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "python package fancyimpute (Rubinsteyn & Feldman, 2017)" but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Unless otherwise specified, we set k = 100, = 0.6, and number of rounds of adding elements r = 4 for adaptive sampling. ... Unless otherwise specified, the parameters are k = 30, radius R = 1.5km, and number of rounds of adding elements for adaptive sampling r = 3. |