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..
Joint Gradient Balancing for Data Ordering in Finite-Sum Multi-Objective Optimization
Authors: Hansi Yang, James Kwok
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation across various datasets with different multi-objective optimization algorithms further demonstrates that Jo GBa can achieve faster convergence and superior final performance than other data ordering strategies. ... Figure 2: Training losses (objective values) of different tasks on NYUv2 data with different data ordering methods. ... Table 1: Test performance (averaged over 3 random seeds) for the three tasks on NYUv2 data. |
| Researcher Affiliation | Academia | Hansi Yang, James Kwok Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Jo GBa: Joint Gradient Balancing for Multi-Objective Optimization. ... Algorithm 2 Online greedy implementation of Balancing(s, gm,k,t). |
| Open Source Code | No | The paper does not contain any explicit statement about making the code available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We consider two data sets that are commonly used for multi-objective optimization in machine learning: (i) NYUv2 (Silberman et al., 2012), an indoor scene data set that involves three different tasks: semantic segmentation, depth estimation, and surface normal prediction. (ii) QM9 (Ramakrishnan et al., 2014), which is a widely used benchmark for graph neural networks predicting 11 properties of molecules. |
| Dataset Splits | No | The paper mentions using NYUv2 and QM9 datasets but does not explicitly describe the training, validation, and test splits used for these datasets. It refers to Appendix B for setup details, but Appendix B does not specify splits either. |
| Hardware Specification | Yes | All experiments are conducted on a server with an Intel Xeon Gold 6342 CPU and an NVIDIA RTX A6000 GPU. |
| Software Dependencies | Yes | We use the PyTorch version 1.10.1 with CUDA version 11.7. |
| Experiment Setup | Yes | Each method is trained for 200 epochs with the Adam optimizer (Kingma & Ba, 2015). We set the learning rate α = 1 10 4 at the beginning of training, and reduce it to 5 10 5 after 100 epochs. The batch size is set to 2 for all methods. ... Each method is trained for 300 epochs with the Adam optimizer (Kingma & Ba, 2015) and we set the learning rate α = 1 10 4 throughout the whole training process. The batch size is set to 120 for all methods. |