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.
Balancing Gradient and Hessian Queries in Non-Convex Optimization
Authors: Deeksha Adil, Brian Bullins, Aaron Sidford, Chenyi Zhang
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 40,123 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,276 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 | Though our work provides new algorithms and tools for critical point computation, there are several limitations to the result. First, this work is primarily theoretical, no practical implementation or experiments are provided, and in certain cases our bounds incur multiple logarithmic factors. |
| Researcher Affiliation | Academia | Institute for Theoretical Studies, ETH Zürich EMAIL Department of Computer Science, Purdue University EMAIL Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1: Critical-or-Progress... Algorithm 2: Restarted-Approx-Hessian-AGD... Algorithm 3: Reduction-To-Unbounded-Hessian |
| Open Source Code | No | First, this work is primarily theoretical, no practical implementation or experiments are provided, and in certain cases our bounds incur multiple logarithmic factors. |
| Open Datasets | No | The paper does not describe the use of any datasets for empirical evaluation, nor does it provide any links or citations to publicly available datasets. The work is theoretical. |
| Dataset Splits | No | The paper does not involve empirical studies or dataset usage, therefore, there is no mention of dataset splits. |
| Hardware Specification | No | First, this work is primarily theoretical, no practical implementation or experiments are provided, and in certain cases our bounds incur multiple logarithmic factors. |
| Software Dependencies | No | The paper is theoretical and does not report on practical implementation or experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | First, this work is primarily theoretical, no practical implementation or experiments are provided, and in certain cases our bounds incur multiple logarithmic factors. |