Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions

Authors: Anupam Gupta, Debmalya Panigrahi, Bernardo Subercaseaux, Kevin Sun

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 anupamg@cs.cmu.edu Debmalya Panigrahi Duke University debmalya@cs.duke.edu Bernardo Subercaseaux Carnegie Mellon University bsuberca@cs.cmu.edu Kevin Sun Duke University ksun@cs.duke.edu
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]