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 [1].
Algorithms with Prediction Portfolios
Authors: Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results are primarily theoretical, however we have included a preliminary empirical validation of our algorithm for min-cost perfect matching in the supplementary material. |
| Researcher Affiliation | Collaboration | Michael Dinitz Johns Hopkins University EMAIL Sungjin Im UC Merced EMAIL Thomas Lavastida University of Texas at Dallas EMAIL Benjamin Moseley Carnegie Mellon University EMAIL Sergei Vassilvitskii Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Minimum cost matching with k predicted dual solutions ... Algorithm 2 Algorithm for combining fractional solutions online for load balancing. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See the supplementary material |
| Open Datasets | No | The paper mentions 'a set of problem instances' and discusses 'sample complexity' for learning predictions, but it does not explicitly provide access information (e.g., links, DOIs, citations with authors/year) for any specific publicly available dataset used in its preliminary empirical validation. |
| Dataset Splits | No | The paper refers to 'preliminary empirical validation' but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper states 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes]' in its self-assessment. However, the provided text does not contain the specific details of the hardware used (e.g., exact GPU/CPU models, memory amounts). |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., programming languages, libraries, frameworks, or solvers with their corresponding versions) that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper discusses algorithmic details and theoretical proofs, but it does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for any empirical validation. |