Online Learning with Imperfect Hints
Authors: Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we develop algorithms and nearly matching lower bounds for online learning with imperfect directional hints. |
| Researcher Affiliation | Collaboration | 1University of Utah, Salt Lake City Utah, USA 2Google Research, Mountain View California, USA 3Boston University, Boston Massachusetts, USA. |
| Pseudocode | Yes | Algorithm 1 OLO with imperfect hints (Procedure ALG) input Hints ht followed by cost vectors ct |
| 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 describe the use of any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |