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..
Revenue Optimization with Approximate Bid Predictions
Authors: Andres Munoz, Sergei Vassilvitskii
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Experiments, Figure 1: (a) Mean revenue of the three algorithms on the linear scenario. (b) Mean revenue of the three algorithms on the bimodal scenario. (c) Mean revenue on auction data. |
| Researcher Affiliation | Industry | Andr es Mu noz Medina Google Research 76 9th Ave New York, NY 10011 Sergei Vassilvitskii Google Research 76 9th Ave New York, NY 10011 |
| Pseudocode | Yes | Algorithm 1. Reserve Inference from Clusters |
| Open Source Code | No | The paper does not contain an unambiguous statement that the source code for the methodology described is publicly available, nor does it provide a direct link to a code repository. |
| Open Datasets | No | For each experiment we generated a training dataset Strain, a holdout set Sholdout and a test set Stest each with 16,000 examples. we collected auction bid data from Ad Exchange for 4 different publisher-advertiser pairs. For each experiment, we extract a random training sample of 20,0000 points as well as a holdout and test sample. The paper describes using self-generated or internally collected data without providing access information or citing publicly available datasets. |
| Dataset Splits | Yes | For each experiment we generated a training dataset Strain, a holdout set Sholdout and a test set Stest each with 16,000 examples. Finally, the choice of hyperparameters γ for the Lipchitz loss and k for the clustering algorithm was done by selecting the best performing parameter over the holdout set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. It only implies computation was performed without specifying the underlying machines. |
| Software Dependencies | No | The paper mentions training a 'linear regressor' but does not specify any software libraries, frameworks, or their version numbers (e.g., TensorFlow, PyTorch, scikit-learn with specific versions). |
| Experiment Setup | Yes | Finally, the choice of hyperparameters γ for the Lipchitz loss and k for the clustering algorithm was done by selecting the best performing parameter over the holdout set. Following the suggestions in [Mohri and Medina, 2014] we chose γ {0.001, 0.01, 0.1, 1.0} and k {2, 4, . . . , 24}. |