Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Complexity of Adversarial Decision Making
Authors: Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
NeurIPS 2022 | Venue PDF | 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. |