On Adaptivity in Information-Constrained Online Learning
Authors: Siddharth Mitra, Aditya Gopalan5199-5206
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study how to adapt to smoothly-varying ( easy ) environments in well-known online learning problems where acquiring information is expensive. For the problem of label efficient prediction, which is a budgeted version of prediction with expert advice, we present an online algorithm whose regret depends optimally on the number of labels allowed and Q (the quadratic variation of the losses of the best action in hindsight), along with a parameter-free counterpart whose regret depends optimally on Q (the quadratic variation of the losses of all the actions). |
| Researcher Affiliation | Academia | Siddharth Mitra,1 Aditya Gopalan2 1Chennai Mathematical Institute, smitra@cmi.ac.in 2Indian Institute of Science, aditya@iisc.ac.in |
| Pseudocode | Yes | Algorithm 1 RESERVOIR SAMPLING, Algorithm 2 ADAPTIVE LABEL EFFICIENT PREDICTION, Algorithm 3 PARAMETER FREE ADAPTIVE LABEL EFFICIENT PREDICTION |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. It discusses 'loss vectors' as part of the problem setup, but these are conceptual and not references to publicly available data. |
| Dataset Splits | No | The paper is theoretical and does not discuss empirical dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |