On the Complexity of Adversarial Decision Making
Authors: Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |