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.
Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond
Authors: Jianjun Yuan, Andrew Lamperski6712-6719
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,582 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,823 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 | The paper focuses on theoretical contributions, providing rigorous characterizations, proving dynamic regret bounds, deriving novel step size rules, and establishing lower bounds. It includes multiple theorems (e.g., Theorem 1, 2, 3, 4, 5, 6, 7, 8) and lemmas, indicating a strong theoretical emphasis. There is no mention of empirical studies, datasets, performance metrics from experiments, or any evaluation on real-world or synthetic data. |
| Researcher Affiliation | Academia | Jianjun Yuan,1 Andrew Lamperski1 1Department of Electrical and Computer Engineering, University of Minnesota 200 Union Street SE, 4-174 Keller Hall Minneapolis, MN 55455, US EMAIL |
| Pseudocode | Yes | Algorithm 1 Discounted Online Newton Step Algorithm 2 Meta-Algorithm |
| Open Source Code | No | The paper does not include any statements about providing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets. Therefore, it does not mention or provide access information for any training datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies with datasets. Therefore, it does not provide any training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not discuss any empirical experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations and algorithm design. It does not mention any specific software or library versions required to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |