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.
Online Improper Learning with an Approximation Oracle
Authors: Elad Hazan, Wei Hu, Yuanzhi Li, Zhiyuan Li
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,715 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,527 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 | Our results are summarized in Table 1 below. We present these two algorithms and their guarantees in Sections 3 and Appendix B. |
| Researcher Affiliation | Collaboration | Elad Hazan Princeton University & Google AI Princeton EMAIL Wei Hu Princeton University EMAIL Yuanzhi Li Stanford University EMAIL Zhiyuan Li Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Mirror Descent using a Projection-and-Separation Oracle; Algorithm 2 Projection-and-Decomposition Oracle, PAD(y, , W, '); Algorithm 3 Online Stochastic Mirror Descent with Barycentric Regularization |
| Open Source Code | No | The paper does not provide any statements about releasing its own source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets, so no dataset availability information for training is provided. |
| Dataset Splits | No | The paper does not discuss empirical validation or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs; it does not mention any specific software dependencies with version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameters or system-level training settings. |