Regularized Online Allocation Problems: Fairness and Beyond
Authors: Santiago Balseiro, Haihao Lu, Vahab Mirrokni
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments confirm the effectiveness of the proposed algorithm and of the regularizers in an internet advertising application. |
| Researcher Affiliation | Collaboration | 1Columbia University, New York, USA 2Google Research, New York, USA 3University of Chicago. |
| Pseudocode | Yes | Algorithm 1: Dual Subgradient Descent Algorithm |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that code is made publicly available. |
| Open Datasets | Yes | We utilize the display advertisement dataset introduced in Balseiro et al. (2014). |
| Dataset Splits | No | The paper describes sampling data from estimated distributions for experiments but does not specify any training/validation/test splits or mention a dedicated validation set. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'cvxpy (Diamond and Boyd, 2016)' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | In the numerical experiments, we implemented Algorithm 1 with weights wj = 2 j and step-size 0.01 T 1/2. ... For each regularization level λ and time horizon T, we randomly choose T samples from their dataset that are fed to Algorithm 1 sequentially. ... We report the average cumulative reward, the average max-min consumption fairness, and the average regret of 100 independent trials in Figure 1 and Figure 2. |