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.
Efficient online algorithms for fast-rate regret bounds under sparsity
Authors: Pierre Gaillard, Olivier Wintenberger
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,389 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,619 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 consider the problem of online convex optimization in two different settings: arbitrary and i.i.d. sequence of convex loss functions. In both settings, we provide efficient algorithms whose cumulative excess risks are controlled with fast-rate sparse bounds. |
| Researcher Affiliation | Academia | Pierre Gaillard INRIA, ENS, PSL Research University Paris, France EMAIL Olivier Wintenberger Sorbonne Université, CNRS, LPSM Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1 Squint BOA with multiple constant learning rates assigned to each parameter... Algorithm 2 SABOA Sparse Acceleration of BOA |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code or provide any links to code repositories. |
| Open Datasets | No | The paper is purely theoretical and does not use or reference any publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits for validation or other purposes. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific details about an experimental setup, such as hyperparameters or training configurations. |