Understanding the Role of Feedback in Online Learning with Switching Costs
Authors: Duo Cheng, Xingyu Zhou, Bo Ji
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We fully characterize the minimax regret in this setting... we propose a generic algorithmic framework, which enables us to design different learning algorithms that can achieve matching upper bounds for both settings based on the amount and type of feedback. |
| Researcher Affiliation | Academia | 1Virginia Tech, Blacksburg, USA 2Wayne State University, Detroit, USA. |
| Pseudocode | Yes | Algorithm 1 Batched Online Mirror Descent with (Optional) Shrinking Dartboard |
| Open Source Code | No | The paper does not provide 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 involve experimental training on datasets, thus no information about public datasets is relevant. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not provide details about experimental setups, hyperparameters, or system-level training settings. |