Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Prediction at the Limit of Zero Temperature
Authors: Mark Herbster, Stephen Pasteris, Shaona Ghosh
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,366 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,592 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Shaona Ghosh ECS University of Southampton Southampton, UK SO17 1BJ EMAIL |
| 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. |