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..
Improving Online Algorithms via ML Predictions
Authors: Manish Purohit, Zoya Svitkina, Ravi Kumar
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental results |
| Researcher Affiliation | Collaboration | Ravi Kumar Google Mountain View, CA EMAIL Manish Purohit Google Mountain View, CA EMAIL Zoya Svitkina Google Mountain View, CA EMAIL |
| 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. |