Continuous Prediction with Experts' Advice
Authors: Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we employ continuous-time stochastic calculus in order to study the discrete-time experts problem. We use these tools to design a continuous-time, parameter-free algorithm with improved guarantees on the quantile regret. We then develop an analogous discrete-time algorithm with a very similar analysis and identical quantile regret bounds. Finally, we design an anytime continuous-time algorithm with regret matching the optimal fixed-time rate when the gains are independent Brownian motions; in many settings, this is the most difficult case. This gives some evidence that, even with adversarial gains, the optimal anytime and fixed-time regrets may coincide. |
| Researcher Affiliation | Collaboration | Nicholas J. A. Harvey EMAIL University of British Columbia Vancouver, BC, Canada Christopher Liaw EMAIL Google Mountain View, CA, USA Victor S. Portella EMAIL University of British Columbia Vancouver, BC, Canada |
| Pseudocode | No | The paper only describes algorithms and methods using mathematical notation and prose, without any dedicated 'Pseudocode' or 'Algorithm' sections, figures, or structured code-like blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to a code repository or mention code in supplementary materials. |
| Open Datasets | No | This paper is theoretical in nature, focusing on continuous-time stochastic calculus and algorithm design. It does not use or refer to any empirical datasets for experiments. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets, therefore, there is no information regarding dataset splits. |
| Hardware Specification | No | The paper presents theoretical work on continuous prediction with experts advice and does not describe any experimental setup or hardware used for computation. |
| Software Dependencies | No | The paper is theoretical and does not involve an experimental implementation or discuss software dependencies with specific version numbers. |
| Experiment Setup | No | This paper is a theoretical work focusing on algorithm design and mathematical analysis. It does not describe an experimental setup with hyperparameters or training configurations. |