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 [1].
Regret Analysis of Repeated Delegated Choice
Authors: Mohammad Hajiaghayi, Mohammad Mahdavi, Keivan Rezaei, Suho Shin
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We obtain sublinear regret upper bounds in various regimes, and derive corresponding lower bounds which implies the tightness of the results. Overall, we bridge a well-known problem in economics to the evolving area of online learning, and present a comprehensive study in this problem. All the proofs can be found in the appendix in the full paper. |
| Researcher Affiliation | Academia | Mohammad Taghi Hajiaghayi, Mohammad Mahdavi, Keivan Rezaei, Suho Shin University of Maryland, College Park EMAIL |
| Pseudocode | Yes | Algorithm 1: DELAYEDPROGERESSIVESEARCH |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention using publicly available datasets for training or empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any validation dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware used for computations or experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers. |
| Experiment Setup | No | The paper describes theoretical settings and algorithms but does not provide details on experimental setup such as hyperparameters or system-level training configurations. |