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.
Subspace Embedding and Linear Regression with Orlicz Norm
Authors: Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 20,230 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,246 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 | Finally, we complement our theoretical results with experimental evaluation of our algorithms. Our experiments reveal that that the solution of regression under the Orlicz norm induced by Huber loss is much better than the solution given by regression under โ1 or โ2 norms, under natural noise distributions in practice. We also perform experiments for Orlicz regression with different Orlicz functions G and show their behavior under different noise settings, thus exhibiting the ๏ฌexibility of our framework. |
| Researcher Affiliation | Academia | 1Computer Science Department, Columbia University, New York City, NY 10027, U.S.A.. |
| Pseudocode | Yes | Algorithm 1 Linear regression with Orlicz norm G |
| Open Source Code | No | The paper does not provide any statements about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Then, we run experiments on real datasets diabetes and glass in UCI repository(Bache & Lichman, 2013). |
| Dataset Splits | No | The paper mentions using simulated data and real datasets but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or citations to standard splits). |
| 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 states 'We use MATLAB s linprog to solve โ1 regression.' However, it does not specify version numbers for MATLAB or the linprog function, which is insufficient for reproducible software dependencies. |
| Experiment Setup | Yes | We chose the parameter ฮด to be 0.75. In all the simulations, we generate matrix A Rn d, ground truth x Rd, and b to be Ax plus some particular noise. We experiment with two n, d combinations, i) n = 200, d = 10 ii) n = 100, d = 75, and 3 noise setting with i) Gaussian noise ii) sparse noise and iii) mixed noise (addition of i) and ii)), altogether 2 3 = 6 setting. |