Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Enhanced Bilevel Optimization via Bregman Distance
Authors: Feihu Huang, Junyi Li, Shangqian Gao, Heng Huang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct data hyper-cleaning task and hyper-representation learning task to demonstrate that our new algorithms outperform related bilevel optimization approaches. |
| Researcher Affiliation | Academia | 1Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States 2College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics, Nanjing, China |
| Pseudocode | Yes | Algorithm 1 Deterministic Bi O-Bre D Algorithm; Algorithm 2 Stochastic Bi O-Bre D (SBi O-Bre D) Algorithm; Algorithm 3 Accelerated Stochastic Bi O-Bre D (ASBi O-Bre D) Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code or links to a code repository. |
| Open Datasets | Yes | We conduct data hyper-cleaning task [39] over the MNIST dataset [25]; 2) hyper-representation learning task [9] over the Omniglot dataset [24]. ... The dataset includes a training set and a validation set where each contains 5000 images. |
| Dataset Splits | Yes | The dataset includes a training set and a validation set where each contains 5000 images. |
| Hardware Specification | Yes | All experiments are averaged over 5 runs and we use a server with AMD EPYC 7763 64-Core CPU and 1 NVIDIA RTX A5000. |
| Software Dependencies | No | The paper does not specify any software names with version numbers. |
| Experiment Setup | Yes | In the experiment, we compare our algorithms (i.e., Bi O-Bre D, SBi O-Bre D, and ASBi O-Bre D) with the following bilevel optimization algorithms: reverse [9]/AID-Bi O [11, 22], AID-CG [12], AID-FP [12], stoc Bi O [22]), MRBO [21], VRBO [21], FSLA [28], SUSTAIN [23], and VR-sa Bi Adam [18]. All experiments are averaged over 5 runs and we use a server with AMD EPYC 7763 64-Core CPU and 1 NVIDIA RTX A5000. ... The detailed experimental setup is described in the Appendix A.1. For hyper-parameters, we perform grid search for our algorithms and other baselines to choose the best setting. ... We use Bregman function ψt(x) = 1/2xT Htx to generate the Bregman distance in our algorithms, where Ht is the adaptive matrix as used in [19], i.e. the exponential moving average of the square of the gradient and we use coefficient 0.99 in all experiments. |