Tracking the Best Expert in Non-stationary Stochastic Environments
Authors: Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 {bahh723, ted0504, cjlu}@iis.sinica.edu.tw |
| 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. |