Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Linear Regression in Dynamic Environments via Discounting
Authors: Andrew Jacobsen, Ashok Cutkosky
ICML 2024 | Venue PDF | 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 <EMAIL>. |
| 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. |