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.
Efficient Pareto Manifold Learning with Low-Rank Structure
Authors: Weiyu Chen, James Kwok
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 24,338 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,309 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 | Extensive experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines, especially on datasets with a large number of tasks. |
| Researcher Affiliation | Academia | Weiyu Chen 1 James T. Kwok 1 1Department of Computer Science and Engineering, The Hong Kong University of Science and Technology. Correspondence to: Weiyu Chen <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 LORPMAN. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | Multi MNIST (Sabour et al., 2017) is a digit classification dataset with two tasks: classification of the top-left digit and classification of the bottom-right digit in each image. |
| Dataset Splits | Yes | We tune the hyperparameters according to the HV value on the validation datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For LORPMAN, we choose the scaling factor s {1, 2, 4, 6} and freeze epoch {4, 6, 8} based on the validation set. For both datasets, the rank r for all layers is set to 8 and the orthogonal regularization coefficient Îťo is set to 1. The learning rate is set to 1e 3 and the batch size is set to 256. |