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.
Structured Estimation with Atomic Norms: General Bounds and Applications
Authors: Sheng Chen, Arindam Banerjee
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,395 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,988 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 | In this paper, we present general upper bounds for such geometric measures, which only require simple information of the atomic norm under consideration, and we establish tightness of these bounds by providing the corresponding lower bounds. We show applications of our analysis to certain atomic norms, especially k-support norm, for which existing result is incomplete. |
| Researcher Affiliation | Academia | Sheng Chen Arindam Banerjee Dept. of Computer Science & Engg., University of Minnesota, Twin Cities EMAIL |
| Pseudocode | Yes | Algorithm 1 Solving polar operator for sp k Input: θ Rp, positive integer k Output: solution u to the polar operator (11) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links or explicit statements of code release) for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets, hence no information about public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation or dataset splits. Thus, no training/test/validation splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or the hardware used to perform them. |
| Software Dependencies | No | The paper is purely theoretical and focuses on mathematical derivations and proofs, not on software implementations with specific version dependencies. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |