Efficiency Through Procrastination: Approximately Optimal Algorithm Configuration with Runtime Guarantees
Authors: Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our concern in this paper is exclusively with worst-case analysis rather than such empirical investigations. |
| Researcher Affiliation | Collaboration | Robert Kleinberg Dept. of Computer Science Cornell University rdk@cs.cornell.edu Kevin Leyton-Brown Dept. of Computer Science University of British Columbia kevinlb@cs.ubc.ca Brendan Lucier Microsoft Research brlucier@microsoft.com |
| Pseudocode | Yes | Algorithm 1: Structured Procrastination (few configs) Algorithm 2: Structured Procrastination (many configs) |
| Open Source Code | No | The paper does not provide any links to source code for the methodology described, nor does it state that the code is available or released. |
| Open Datasets | No | The paper is theoretical and does not use or provide access information for any public datasets for empirical evaluation. Example 2.2 uses a hypothetical scenario with 1000 input instances, not a real dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. Therefore, no training/test/validation splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper focuses on theoretical guarantees and algorithm design; it does not describe an experimental setup with hyperparameters or training settings for empirical evaluation. |