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..
Understanding the Role of Feedback in Online Learning with Switching Costs
Authors: Duo Cheng, Xingyu Zhou, Bo Ji
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We fully characterize the minimax regret in this setting... we propose a generic algorithmic framework, which enables us to design different learning algorithms that can achieve matching upper bounds for both settings based on the amount and type of feedback. |
| Researcher Affiliation | Academia | 1Virginia Tech, Blacksburg, USA 2Wayne State University, Detroit, USA. |
| Pseudocode | Yes | Algorithm 1 Batched Online Mirror Descent with (Optional) Shrinking Dartboard |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experimental training on datasets, thus no information about public datasets is relevant. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not provide details about experimental setups, hyperparameters, or system-level training settings. |