Efficient Pareto Manifold Learning with Low-Rank Structure
Authors: Weiyu Chen, James Kwok
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 <wchenbx@cse.ust.hk>. |
| 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 λo is set to 1. The learning rate is set to 1e 3 and the batch size is set to 256. |