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..
Contract Scheduling with Distributional and Multiple Advice
Authors: Spyros Angelopoulos, Marcin Bienkowski, Christoph Dรผrr, Bertrand Simon
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Last, we present an experimental evaluation that confirms the theoretical findings, and illustrates the performance improvements that can be attained in practice. |
| Researcher Affiliation | Academia | 1LIP6, Sorbonne University 2University of Wroclaw 3CNRS 4IN2P3 Computing Center |
| Pseudocode | No | The paper describes algorithms verbally and through mathematical derivations (e.g., in Theorem 9 'The above observation leads to the following algorithm for finding an optimal schedule'), but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating that its source code is publicly available. |
| Open Datasets | No | The paper evaluates its algorithms using generated distributions and random values ('We first consider, as distributional advice ยต, a normal distribution...', 'advice chosen according to U[0.95t, 1.05t]', 'generate P as k values chosen independently and uniformly at random'), rather than a specific publicly available dataset with concrete access information for training. |
| Dataset Splits | No | The paper does not specify traditional training, validation, or test dataset splits. It evaluates its algorithms on generated distributions and random problem instances. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies along with their version numbers. |
| Experiment Setup | No | The paper describes the parameters for the generated input distributions used in its experimental evaluation (e.g., 'normal distribution... with mean m, and standard deviation ฯ', 'uniform distribution in [0.95t, 1.05t]', 'k values chosen independently and uniformly at random'), but these are not hyperparameters or system-level training settings typical for machine learning models. |