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 Prediction with Selfish Experts
Authors: Tim Roughgarden, Okke Schrijvers
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 6 we show simulations that indicate that different IC methods show similar regret behavior, and that their regret is substantially better than that of the non-IC standard algorithms, suggesting that the worst-case characterization we prove holds more generally. (Section 6: Simulations, Figure 1a, Figure 1b, Table 1) |
| Researcher Affiliation | Academia | Tim Roughgarden Department of Computer Science Stanford University Stanford, CA 94305 EMAIL Okke Schrijvers Department of Computer Science Stanford University Stanford, CA 94305 EMAIL |
| Pseudocode | No | The paper describes algorithms (Weighted Majority (WM) and Randomized Weighted Majority (RWM)) and their update rules verbally and mathematically, but it does not present them in a formal pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions a "simple two-state hidden Markov model (HMM)" as a data-generating process for simulations but does not provide any access information (link, citation, repository) for the specific dataset generated or used in their simulations. |
| Dataset Splits | No | The paper describes simulations using a data-generating process but does not specify any training, validation, or test dataset splits for these simulations. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the simulations (e.g., GPU/CPU models, memory, cloud resources). |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers used in the simulations. |
| Experiment Setup | Yes | For the IC methods, experts report p(t)i , for the standard algorithm p(t)i = 1 if b(t)i = 0 otherwise. The y axis is the ratio of the total loss of each of the algorithms to the performance of the best expert at that time. The plot is for 10 experts, T = 10, 000, = 10 2, and the randomized versions of the algorithms, averaged over 30 runs. Varying model parameters and the deterministic version show similar results. |