A Field Guide for Pacing Budget and ROS Constraints
Authors: Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical findings empirically by showing that the min-pacing algorithm performs almost as well as the canonical dual-based algorithm on a semisynthetic dataset that was generated from a large online advertising platform s auction data. Empirical evaluation. Section 4 explains in detail our evaluation methodology, including how we construct our semi-synthetic dataset, how we obtain the different quantities in our optimization formulation (1) based on real auction data. |
| Researcher Affiliation | Collaboration | 1Google Research, USA 2Columbia University, NYC, USA. Correspondence to: Di Wang <wadi@google.com>. |
| Pseudocode | Yes | Algorithm 1 Dual-Optimal Pacing |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | For confidentiality and advertiser privacy reasons, we use a semi-synthetic dataset based on actual advertising auctions from an online platform... Our dataset includes 105 randomly selected campaigns... The paper does not provide a link or specific access information for this semi-synthetic dataset. |
| Dataset Splits | No | The paper mentions dividing the day into 10-minute periods (T=144) and simulating algorithms 10 times, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | For each campaign, we set the budget constraint (i.e. ρT in (1)) using its actual daily budget B. We divide the day into 10-minute periods and use T = 144. For each campaign, we simulate an algorithm 10 times... For each algorithm, we do a grid search over the step-sizes used in the dual variables updates. |