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.
On Penalty-based Bilevel Gradient Descent Method
Authors: Han Shen, Tianyi Chen
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 32,358 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,502 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 | The experimental results showcase the efficiency of the proposed algorithm. The code is available on Git Hub (link). ... Finally, we empirically showcase the performance, computation and memory efficiency of the proposed algorithm in comparison with several competitive baselines. ... In this section, we test PBGD in the data hyper-cleaning task (Franceschi et al., 2017; Shaban et al., 2019). |
| Researcher Affiliation | Academia | 1Department of ECSE, Rensselaer Polytechnic Institute, Troy, NY, USA. |
| Pseudocode | Yes | Algorithm 1 PBGD: Penalized bilevel GD ... Algorithm 2 V-PBGD: Function value gap based PBGD ... Algorithm 3 V-PBGD under lower-level constraint |
| Open Source Code | Yes | The code is available on Git Hub (link). ... The code is available on github (link). |
| Open Datasets | Yes | Adopting the settings in (Franceschi et al., 2017; Liu et al., 2021b; Shaban et al., 2019), we randomly split the MNIST data-set into a training data-set of size 5000, a validation set of size 5000 and a test set of size 10000; and pollute 50% of the training data with uniformly drawn labels. |
| Dataset Splits | Yes | we randomly split the MNIST data-set into a training data-set of size 5000, a validation set of size 5000 and a test set of size 10000 |
| Hardware Specification | No | The paper mentions "GPU memory" in Table 3 but does not specify any particular GPU model, CPU, or other detailed hardware specifications used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | We then run V-PBGD with γ = 10 for 1000 random initial points (x1, y1) and plot the last iterates in Figure 2a (right). ... we randomly split the MNIST data-set into a training data-set of size 5000, a validation set of size 5000 and a test set of size 10000; and pollute 50% of the training data with uniformly drawn labels. Then we run the algorithms with a linear model and an MLP network. |