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.
Density Corrected Sparse Recovery when R.I.P. Condition Is Broken
Authors: Ming Lin, Zhengzhong Lan, Alexander G. Hauptmann
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,777 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,543 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that when features exhibit cluster structures, which often happens in real applications, we are able to recover the sparse vector consistently. The consistency comes from our proposed density correction algorithm, which removes the variance of estimated cluster centers using cluster density. The proposed algorithm converges geometrically, achieves nearly optimal recovery bound O(s2 log(d)) where s is the sparsity and d is the nominal dimension. |
| Researcher Affiliation | Academia | Ming Lin, Zhengzhong Lan, Alexander G. Hauptmann Carnegie Mellon University Pittsburgh, PA, USA |
| Pseudocode | Yes | Algorithm 1 Density Corrected Sparse Recovery (DCSR) |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with specific datasets. It discusses 'training instances' in a general context, but no specific public dataset is mentioned or linked. |
| Dataset Splits | No | The paper is theoretical and does not include empirical experiments with validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, such as hyperparameters or training settings. |