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.
Improved Active Learning via Dependent Leverage Score Sampling
Authors: Atsushi Shimizu, Xiaoou Cheng, Christopher Musco, Jonathan Weare
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 27,947 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,220 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 | We experimentally evaluate our pivotal sampling methods on active regression problems with lowdimensional structure. |
| Researcher Affiliation | Academia | Atsushi Shimizu, Xiaoou Cheng, Christopher Musco, Jonathan Weare New York University EMAIL |
| Pseudocode | Yes | Algorithm 1 Binary Tree Based Pivotal Sampling (Deville and Tille, 1998) |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | No | For both problems, we construct A by uniformly selecting n = 105 data points in the 2-dimensional parameter range of interest. We then add all polynomial features of degree p = 12 as discussed in Section 1.2. We compute sampled entries from the target vector b using standard MATLAB routines. |
| Dataset Splits | No | The paper discusses 'train' and 'test' data conceptually but does not provide explicit details about a 'validation' split or how it was used in the experimental setup. |
| Hardware Specification | No | The Acknowledgements section mentions 'NYU IT for the use of the Greene computing cluster'. However, no specific hardware details such as GPU/CPU models, memory, or detailed cluster configurations are provided. |
| Software Dependencies | No | The paper mentions the use of 'standard MATLAB routines' for computing sampled entries from the target vector. However, no specific version numbers for MATLAB or any other software dependencies are provided. |
| Experiment Setup | Yes | We report median normalized error A x b 2 2/ b 2 2 after 1000 trials. By drawing more samples from b, the errors of all methods eventually converge to the optimal error Ax b 2 2/ b 2 2, but clearly the pivotal method requires less samples to achieve a given level of accuracy, confirming the benefits of spatially-aware sampling. |