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.
Discriminative State Space Models
Authors: Vitaly Kuznetsov, Mehryar Mohri
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,223 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,805 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 | We conclude with some promising preliminary experimental results in Appendix D. |
| Researcher Affiliation | Collaboration | Vitaly Kuznetsov Google Research New York, NY 10011, USA EMAIL Mehryar Mohri Courant Institute and Google Research New York, NY 10011, USA EMAIL |
| Pseudocode | Yes | Figure 1: Pseudocode of the BOOSTSM algorithm. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions "observed realization (X1, Y1), . . . , (XT , YT ) of some stochastic process" and "preliminary experimental results in Appendix D" but does not provide concrete access information (link, citation, repository) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions "preliminary experimental results in Appendix D" but does not provide specific experimental setup details, such as concrete hyperparameter values, training configurations, or system-level settings, in the main text. |