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.
Hardness and Algorithms for Robust and Sparse Optimization
Authors: Eric Price, Sandeep Silwal, Samson Zhou
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 28,042 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,326 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 explore algorithms and limitations for sparse optimization problems such as sparse linear regression and robust linear regression. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, The University of Texas at Austin. 2Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology. 3Computer Science Department, Carnegie Mellon University. |
| Pseudocode | Yes | Algorithm 1 Sparse Regression Upper Bound |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithms and hardness proofs; it does not describe experiments using datasets or provide access information for any dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (training, validation, or test). |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |