Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Ad Allocation with Predictions
Authors: Fabian Spaeh, Alina Ene
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our algorithm on synthetic and real-world data on a wide range of predictions. Our algorithm is consistently outperforming the worst-case algorithm without predictions. |
| Researcher Affiliation | Academia | Fabian Spaeh Department of Computer Science Boston University EMAIL Alina Ene Department of Computer Science Boston University EMAIL |
| Pseudocode | Yes | Algorithm 1 Exponential Averaging with Predictions |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We generate two instances for Display Ads based on the real-word datasets i Pin You (Zhang et al., 2014) and Yahoo (Yahoo, 2011). |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility, as the problem is online with impressions arriving sequentially. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, specific solver versions) needed to replicate the experiments. |
| Experiment Setup | Yes | For each predictor, we show the consistency (left) and robustness (right) for varying α. Figure 4 shows results for α = 5 with predictions of different quality, as described in the figure caption. We vary the sample fraction ϵ [0, 1] for the dual base algorithm and p [0, 1] for random and biased corruptions. We use synthetic data with 12 advertisers and 2000 impressions of 10 types, where we report the same quantities as in Figure 3. We assume a constant budget for each advertiser of 10 impressions as it makes for an interesting instance. |