Online Linear Regression in Dynamic Environments via Discounting
Authors: Andrew Jacobsen, Ashok Cutkosky
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop algorithms for online linear regression which achieve optimal static and dynamic regret guarantees even in the complete absence of prior knowledge. We present a novel analysis showing that a discounted variant of the Vovk-Azoury-Warmuth forecaster achieves dynamic regret of the form RT (u) O (dlog(T) d P γ T (u)T), where P γ T (u) is a measure of variability of the comparator sequence, and show that the discount factor achieving this result can be learned on-the-fly. We show that this result is optimal by providing a matching lower bound. |
| Researcher Affiliation | Academia | 1Department of Computing Science, University of Alberta, Edmonton, Canada 2Department of Electrical and Computer Engineering, Boston University, Boston, Massachussetts. Correspondence to: Andrew Jacobsen <ajjacobs@ualberta.ca>. |
| Pseudocode | Yes | Algorithm 1: Discounted VAW Forecaster |
| Open Source Code | No | The paper does not provide any statements about open-sourcing code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets for experimental evaluation. |
| Dataset Splits | No | The paper does not describe dataset splits for training, validation, or testing as it focuses on theoretical analysis rather than empirical evaluation. |
| Hardware Specification | No | The paper is theoretical and does not discuss specific hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, as it focuses on theoretical algorithm design and analysis. |
| Experiment Setup | No | The paper does not describe a specific experimental setup, hyperparameters, or training configurations, as it focuses on theoretical algorithm design and analysis. |