Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Multiple Markov Chains via Adaptive Allocation
Authors: Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,341 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,691 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 study the problem of learning the transition matrices of a set of Markov chains from a single stream of observations on each chain. We present a novel learning algorithm that efficiently balances exploration and exploitation intrinsic to this problem, without any prior knowledge of the chains. We provide finite-sample PAC-type guarantees on the performance of the algorithm. Further, we show that our algorithm asymptotically attains an optimal loss. All proofs are provided in the supplementary material. |
| Researcher Affiliation | Academia | Mohammad Sadegh Talebi SequeL Team, Inria Lille Nord Europe EMAIL Odalric-Ambrym Maillard SequeL Team, Inria Lille Nord Europe EMAIL |
| Pseudocode | Yes | Algorithm 1 BA-MC Bandit Allocation for Markov Chains |
| Open Source Code | No | The paper is theoretical and focuses on algorithm design and proofs; it does not mention the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experimental data or datasets. Therefore, it does not mention public availability of a training dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. There is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments or the hardware used to run them. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies or versions for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and presents an algorithm with performance guarantees. It does not describe an experimental setup with hyperparameters or system-level training settings. |