Pandora’s Problem with Deadlines
Authors: Ben Berger, Tomer Ezra, Michal Feldman, Federico Fusco
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is an efficient thresholdbased strategy that achieves a constant approximation relative to the performance of the optimal strategy for the deadlines setting. The paper also contains extensive proofs and lemmas, indicating theoretical work. The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24) |
| Researcher Affiliation | Collaboration | 1 Offchain Labs, Inc. 2 Simons Laufer Mathematical Sciences Institute 3Tel Aviv University 4 Microsoft ILDC 5Department of Computer, Control and Management Engineering Antonio Ruberti , Sapienza University of Rome |
| Pseudocode | No | The paper describes the algorithmic steps and logic verbally and through mathematical formulations (e.g., 'In this section we present our main result: an efficient strategy... in three steps'), but it does not include formal pseudocode blocks or figures. |
| Open Source Code | No | The paper does not provide any information about open-source code availability, specific repository links, or statements about code release in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets. It discusses 'box distributions' and 'random variables' in a theoretical context, but does not mention specific datasets or their public availability. |
| Dataset Splits | No | As the paper is theoretical and does not conduct empirical experiments, there is no discussion of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or hardware used. No specific hardware details (like GPU/CPU models or memory) are provided. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific solvers). |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or training configurations, as it does not describe empirical experiments. |