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..
Efficiency Through Procrastination: Approximately Optimal Algorithm Configuration with Runtime Guarantees
Authors: Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier
IJCAI 2017 | Venue PDF | 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 EMAIL Kevin Leyton-Brown Dept. of Computer Science University of British Columbia EMAIL Brendan Lucier Microsoft Research EMAIL |
| 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. |