Revenue Optimization with Approximate Bid Predictions
Authors: Andres Munoz, Sergei Vassilvitskii
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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}. |