Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
No-Regret and Incentive-Compatible Online Learning
Authors: Rupert Freeman, David Pennock, Chara Podimata, Jennifer Wortman Vaughan
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,822 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,921 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on datasets from Five Thirty Eight, our algorithms have regret comparable to classic no-regret algorithms, which are not incentive-compatible. |
| Researcher Affiliation | Collaboration | 1Darden School of Business, University of Virginia. 2DIMACS, Rutgers University. 3Harvard University. 4Microsoft Research NYC. |
| Pseudocode | Yes | Algorithm 1 WSU-UX with parameters η and γ such that 0 < η, γ < 1/2 and ηK/γ 1/2. |
| Open Source Code | Yes | Our code and the datasets we use are publicly available online. Code: https://github.com/charapod/noregr-and-ic. |
| Open Datasets | Yes | Our code and the datasets we use are publicly available online. Datasets: https://github.com/fivethirtyeight/nfl-elo-game |
| Dataset Splits | No | The paper describes the number of games and how forecasters were sampled for different values of K, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper states that code is available but does not specify any software dependencies or their version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | For WSU, 'for step size η = p ln(K)/T yields regret R 2 T ln K'. For WSU-UX, 'For T K ln K and parameters η = ln K 4K1/2T 2/3 and γ = K ln K / 4T 1/3, WSU-UX is incentive compatible and yields regret R 2(4T)2/3(K ln K)1/3.' |