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..
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. |