Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
Authors: Wouter M. Koolen, Peter Grünwald, Tim van Erven
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that these algorithms attain fast rates in their respective settings both in expectation and with high probability. |
| Researcher Affiliation | Academia | Wouter M. Koolen Centrum Wiskunde & Informatica Science Park 123, 1098 XG Amsterdam, the Netherlands wmkoolen@cwi.nl Peter Grünwald CWI and Leiden University pdg@cwi.nl Tim van Erven Leiden University Niels Bohrweg 1, 2333 CA Leiden, the Netherlands tim@timvanerven.nl |
| Pseudocode | No | The paper describes algorithms (Squint and Meta Grad) and their properties, but it does not include pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code is available. |
| Open Datasets | No | The paper discusses theoretical settings involving data and distributions (e.g., "i.i.d. losses drawn from a distribution"), but it does not specify any particular dataset, nor does it provide information about its public availability or access. |
| Dataset Splits | No | The paper does not mention training, validation, or test dataset splits. It focuses on theoretical guarantees. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or version numbers. |
| Experiment Setup | No | The paper does not describe any experimental setup details such as hyperparameters or training configurations, as it is a theoretical work. |