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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Prospective Side Information for Latent MDPs
Authors: Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 34,211 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,157 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We then establish that any sample efficient algorithm must suffer at least โฆ(K2/3)-regret, as opposed to standard โฆ(K) lower bounds. We design an algorithm with a matching upper bound that depends only polynomially on the problem parameters. In this section, we present our algorithmic results as well as lower bound analysis. |
| Researcher Affiliation | Collaboration | 1Wisconsin Institute for Discovery, Wisconsin, USA 2Meta AI, New York, USA 3Electrical Engineering, Technion, Haifa, Israel 4NVIDIA 5Electrical and Computer Engineering, University of Texas at Austin, Texas, USA. |
| Pseudocode | Yes | Algorithm 1 Regret Minimization within ฮ blind, Algorithm 2 Pure Exploration for LMDP-ฮจ |
| Open Source Code | No | The paper does not contain any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments or use any specific dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments or specify dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments or specify any hardware used. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments or provide details on experimental setup, hyperparameters, or training configurations. |