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.
Isotropic Noise in Stochastic and Quantum Convex Optimization
Authors: Annie Marsden, Liam O'Carroll, Aaron Sidford, Chenyi Zhang
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 39,007 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,521 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 results are theoretical. |
| Researcher Affiliation | Collaboration | Google Deepmind, EMAIL Stanford University, EMAIL |
| Pseudocode | Yes | Algorithm 1: Inexact Line Search(h, ϵ ) |
| Open Source Code | No | Our results are theoretical. |
| Open Datasets | No | Our results are theoretical. |
| Dataset Splits | No | Our results are theoretical. |
| Hardware Specification | No | Our results are theoretical. |
| Software Dependencies | No | Our results are theoretical. |
| Experiment Setup | No | Our results are theoretical. |