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].
Tracking the Best Expert in Non-stationary Stochastic Environments
Authors: Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We not only propose algorithms with upper bound guarantee, but prove their matching lower bounds as well. Our effort mostly focuses on characterizing the achievable (minimax) regrets, and most of our upper bounds are achieved by algorithms which need the knowledge of the related parameters and may not be practical. |
| Researcher Affiliation | Academia | Chen-Yu Wei Yi-Te Hong Chi-Jen Lu Institute of Information Science Academia Sinica, Taiwan EMAIL |
| Pseudocode | Yes | Algorithm 1 Rerun-UCB-V, Algorithm 2 Full-information GD-based algorithm, Algorithm 3 Optimistic-Adapt-ML-Prod |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code publicly available, nor does it provide links to a code repository. |
| Open Datasets | No | The paper describes theoretical results and algorithms, and does not report on experiments conducted using a specific dataset. Therefore, there is no mention of a publicly available dataset for training. |
| Dataset Splits | No | As a theoretical paper focusing on algorithms and regret bounds, it does not describe experimental data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical derivations and algorithm design; it does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations and algorithm design; it does not mention any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical derivations and algorithm design; it does not describe any specific experimental setup details such as hyperparameters or training configurations. |