Online Prediction with Selfish Experts
Authors: Tim Roughgarden, Okke Schrijvers
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 tim@cs.stanford.edu Okke Schrijvers Department of Computer Science Stanford University Stanford, CA 94305 okkes@cs.stanford.edu |
| 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. |