Adaptation to Easy Data in Prediction with Limited Advice
Authors: Tobias Sommer Thune, Yevgeny Seldin
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive an online learning algorithm with improved regret guarantees for easy loss sequences. ... The proposed Second Order Difference Adjustments (SODA) algorithm requires no prior knowledge of the effective range of the losses, ε, and achieves an O(εKT ln K) + O(εK 4T) expected regret guarantee, where T is the time horizon and K is the number of actions. We also provide a regret lower bound of Ω(εTK), which almost matches the upper bound. ... The paper is structured in the following way. In Section 2 we lay out the problem setting. In Section 3 we present the algorithm and in Section 4 the main results about the algorithm. Proofs of the main results are presented in Section 5. |
| Researcher Affiliation | Academia | Tobias Sommer Thune Department of Computer Science University of Copenhagen tobias.thune@di.ku.dk Yevgeny Seldin Department of Computer Science University of Copenhagen seldin@di.ku.dk |
| Pseudocode | Yes | Algorithm 1: Second Order Difference Adjustments (SODA) |
| Open Source Code | No | The paper does not provide any link to open-source code for the described algorithm or explicitly state that it is being released. |
| Open Datasets | No | This is a theoretical paper and does not mention the use of any datasets for training or evaluation, nor does it provide access information for any datasets. |
| Dataset Splits | No | This is a theoretical paper and does not mention the use of any datasets for validation. |
| Hardware Specification | No | As a theoretical paper, no specific hardware used for experiments is mentioned. |
| Software Dependencies | No | As a theoretical paper focused on algorithm design and proofs, no specific software dependencies or versions are mentioned. |
| Experiment Setup | No | As a theoretical paper, there are no experimental setup details, hyperparameters, or training configurations provided. |