Online Ad Procurement in Non-stationary Autobidding Worlds
Authors: Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that our algorithm achieves low regret in many worlds when procurement outcomes are generated through procedures that are stochastic, adversarial, adversarially corrupted, periodic, and ergodic, respectively, without having to know which procedure is the ground truth. Finally, we emphasize that our proposed algorithm and theoretical results extend beyond the applications of online advertising. |
| Researcher Affiliation | Academia | Jason Cheuk Nam Liang MIT jcnliang@mit.edu Haihao Lu University of Chicago haihao.lu@chicagobooth.edu Baoyu Zhou University of Michigan zbaoyu@umich.edu |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that source code is open-source or available. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any publicly available dataset for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |