Maximizing the Success Probability of Policy Allocations in Online Systems
Authors: Artem Betlei, Mariia Vladimirova, Mehdi Sebbar, Nicolas Urien, Thibaud Rahier, Benjamin Heymann
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct comprehensive experiments both on synthetic and real-world data to evaluate its performance. The results demonstrate that our proposed algorithm outperforms conventional expected-value maximization algorithms in terms of success rate. |
| Researcher Affiliation | Industry | 1 Criteo AI Lab, France 2 Criteo Ad Landscape, France {a.betlei, m.vladimirova, m.sebbar, n.urien, t.rahier, b.heymann}@criteo.com |
| Pseudocode | Yes | Algorithm 1: Success Proba Max |
| Open Source Code | Yes | source code3 is published to reproduce all the empirical results. 3https://github.com/criteo-research/success-proba-max |
| Open Datasets | Yes | CRITEO-UPLIFT v2 (Diemert et al. 2021) is provided by the Ad Tech company Criteo. |
| Dataset Splits | No | The paper states: 'Finally, we randomly partitioned dataset into two equal parts for train and test.' It does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper mentions using the JAX framework but does not specify any hardware details like GPU/CPU models or other computing infrastructure used for experiments. |
| Software Dependencies | No | The paper mentions using the 'JAX framework' but does not provide specific version numbers for JAX or any other software dependencies. |
| Experiment Setup | Yes | Hyperparameters used for the methods are provided in Supplementary material and source code3 is published to reproduce all the empirical results. |