Unlocking Global Optimality in Bilevel Optimization: A Pilot Study
Authors: Quan Xiao, Tianyi Chen
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments corroborate the theoretical findings, demonstrating convergence to global minimum in both cases. |
| Researcher Affiliation | Academia | Quan Xiao Rensselaer Polytechnic Institute Troy, NY 12180, United States EMAIL Tianyi Chen Rensselaer Polytechnic Institute Troy, NY 12180, United States EMAIL |
| Pseudocode | Yes | Algorithm 1 PBGD in Jacobi fashion ... Algorithm 2 PBGD in Gauss-Seidel fashion |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code or links to a code repository. |
| Open Datasets | No | The paper describes generating synthetic datasets for its numerical experiments (e.g., "generate data matrix Xtrn RN m, Xval RN m from Gaussian distribution N(5, 0.01)" in sections H.1 and H.2), but does not provide concrete access information (links, DOIs, citations) to a publicly available or open dataset. The generated data itself is not stated to be made public. |
| Dataset Splits | Yes | H.1 Representation Learning: Considering the overparameterized and wide neural network case, we choose N = 30, N = 20, m = 40, n = 10, h = 300. H.2 Data Hyper-cleaning: Considering the overparameterized linear regression with a small clean validation dataset and a large dirty training dataset, we choose N = 100, N = 10, m = 200, n = 10. |
| 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 in the 'Numerical Experiments' section or elsewhere. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | H.1 Representation Learning: ...we choose N = 30, N = 20, m = 40, n = 10, h = 300. First, we respectively generate data matrix Xtrn RN m, Xval RN m from Gaussian distribution N(5, 0.01) and N( 3, 0.01)... We select the best stepsizes α, β and the number of inner loop Tk = T by grid search. H.2 Data Hyper-cleaning: ...we choose N = 100, N = 10, m = 200, n = 10. First, we respectively generate data matrix Xtrn RN m, Xval RN m from Gaussian distribution N(5, 0.01) and N( 3, 0.01)... |