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.
Dynamic Tensor Product Regression
Authors: Aravind Reddy, Zhao Song, Lichen Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,184 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,772 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is theoretical and it provides a dynamic algorithm for tensor product-typed problems. We analyze the runtime complexity of our algorithm, which relates to energy consumption in practice. We do not foresee potential negative societal impact of our work. If you are including theoretical results... Did you state the full set of assumptions of all theoretical results? [Yes] Please refer to Section 3.2. (b) Did you include complete proofs of all theoretical results? [Yes] Please refer to Section B, C, D, and E in the supplementary materials. If you ran experiments... [N/A] |
| Researcher Affiliation | Collaboration | Aravind Reddy Zhao Song Lichen Zhang EMAIL. Northwestern University. EMAIL. Adobe Research. EMAIL. MIT. (Author names in alphabetical order, equal contribution) |
| Pseudocode | Yes | Algorithm 1 Our dynamic tree data structure |
| 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 is theoretical and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| 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] |