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.
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization
Authors: Feihu Huang
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,559 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,538 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 conduct some numerical experiments on the bilevel PL game and hyper-representation learning task to demonstrate efficiency of our proposed method. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China 2MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China. |
| Pseudocode | Yes | Algorithm 1 Hessian/Jacobian-free Bilevel Optimization (i.e, HJFBi O) Algorithm |
| Open Source Code | No | No explicit statement or link to open-source code for the described methodology is provided in the paper. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments ('samples {pi}n i=1, {qi}n i=1, {r1 i }n i=1 and {r2 i }n i=1 are independently drawn from normal distributions' and 'We randomly generate n = 30 d samples of sensing matrices {Ci}n i=1 from standard normal distribution, and then compute the corresponding no-noise labels oi = Ci, H '). It does not provide access information for a publicly available or open dataset. |
| Dataset Splits | Yes | We split all samples into two dataset: a train dataset Dt with 40% data and a validation dataset Dv with 60% data. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running the experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are provided in the paper. |
| Experiment Setup | Yes | For fair comparison, we set a basic learning rate as 0.01 for all algorithms. In our HJFBi O method, we set δϵ = 10 5. |