Improving Online Algorithms via ML Predictions
Authors: Manish Purohit, Zoya Svitkina, Ravi Kumar
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental results |
| Researcher Affiliation | Collaboration | Ravi Kumar Google Mountain View, CA ravi.k53@gmail.com Manish Purohit Google Mountain View, CA mpurohit@google.com Zoya Svitkina Google Mountain View, CA zoya@cs.cornell.edu |
| Pseudocode | Yes | Algorithm 1: A simple 1-consistent algorithm |
| 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 | For all experiments, we set the cost of buying to b = 100 and the actual number of skiing days x is a uniformly drawn integer from [1, 4b]. The predicted number of days y is simulated as y = x + ϵ where ϵ is drawn from a normal distribution with mean 0 and standard deviation σ. We generate a synthetic dataset with 50 jobs where the processing time of each job is sampled independently from a Pareto distribution with an exponent of α = 1.1. The paper describes how the data was generated or simulated, but does not provide concrete access information (link, DOI, specific citation) for a publicly available dataset. |
| Dataset Splits | No | The paper describes experiments run over '10000 independent trials' and '1000 independent trials' and varying a parameter 'σ', which indicates repeated simulations for evaluation. However, it does not specify explicit training/validation/test dataset splits, as the focus is on the performance of online algorithms with given predictions rather than training a machine learning model. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models, processor types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | For all experiments, we set the cost of buying to b = 100 and the actual number of skiing days x is a uniformly drawn integer from [1, 4b]. The predicted number of days y is simulated as y = x + ϵ where ϵ is drawn from a normal distribution with mean 0 and standard deviation σ. We set λ = 0.5 for the deterministic algorithm... We set λ = ln(3/2) for the randomized algorithm. ... we set the predicted job length yi = xi + ϵi, where ϵi is drawn from a normal distribution with mean zero and standard deviation σ. |