Online Prediction at the Limit of Zero Temperature
Authors: Mark Herbster, Stephen Pasteris, Shaona Ghosh
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the appendices we provide proofs as well as preliminary experimental results. |
| Researcher Affiliation | Academia | Mark Herbster Stephen Pasteris Department of Computer Science University College London London WC1E 6BT, England, UK {m.herbster,s.pasteris}@cs.ucl.ac.uk Shaona Ghosh ECS University of Southampton Southampton, UK SO17 1BJ ghosh.shaona@gmail.com |
| Pseudocode | Yes | Figure 1: Computing the Picard-Queyranne graph and Figure 3: Longest-path and 0-Ising online prediction both contain structured algorithm steps. |
| Open Source Code | No | The paper does not provide any specific links to source code or explicit statements about code availability. |
| Open Datasets | No | The paper mentions 'preliminary experimental results' but does not describe any specific datasets used for these experiments or provide access information for any public datasets. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (training, validation, test) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (e.g., hyperparameter values, training configurations) in the main text. |