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.
On the Complexity of Adversarial Decision Making
Authors: Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,533 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,941 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 main result is to show via new upper and lower bounds that the Decision-Estimation Coefficient, a complexity measure introduced by Foster et al. [17] in the stochastic counterpart to our setting, is necessary and sufficient to obtain low regret for adversarial decision making. |
| Researcher Affiliation | Collaboration | Dylan J. Foster EMAIL Alexander Rakhlin EMAIL Ayush Sekhari EMAIL Karthik Sridharan EMAIL |
| Pseudocode | Yes | Algorithm 1 High-Probability Exploration-by-Optimization (Ex O+) |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing code for the described methodology or a link to a code repository. |
| Open Datasets | No | This is a theoretical paper. It does not conduct empirical studies with datasets; therefore, it does not use a training dataset. |
| Dataset Splits | No | This is a theoretical paper. It does not conduct empirical studies with datasets; therefore, it does not specify validation splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe empirical experiments requiring specific hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not describe empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training settings. |