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].
Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions
Authors: Anupam Gupta, Debmalya Panigrahi, Bernardo Subercaseaux, Kevin Sun
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Researcher Affiliation | Academia | Anupam Gupta Carnegie Mellon University EMAIL Debmalya Panigrahi Duke University EMAIL Bernardo Subercaseaux Carnegie Mellon University EMAIL Kevin Sun Duke University EMAIL |
| Pseudocode | Yes | Algorithm 1: MARKER Algorithm 2: STRIKER Algorithm 3: Explore Segment Algorithm 4: Exploit Segment |
| Open Source Code | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Dataset Splits | No | 3. If you ran experiments... (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |
| Hardware Specification | No | 3. If you ran experiments... (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers, as it is a theoretical paper and states [N/A] for experiments. |
| Experiment Setup | No | 3. If you ran experiments... (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |