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..
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
Authors: Sattar Vakili, Julia Olkhovskaya
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove the first order-optimal regret guarantees under a general setting. Our results show a significant polynomial in the number of episodes improvement over the state of the art. |
| Researcher Affiliation | Collaboration | Sattar Vakili Media Tek Research Cambridge, UK EMAIL Julia Olkhovskaya TU Delft Delft, the Netherlands EMAIL |
| Pseudocode | Yes | A pseudocode is provided in Algorithm 1. |
| Open Source Code | No | The paper does not provide any statement about releasing its source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe conducting experiments with a specific dataset. Therefore, it does not provide access information for a dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers required to replicate experimental results. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |