Conflict-Averse Gradient Descent for Multi-task learning
Authors: Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a series of challenging multi-task supervised learning and reinforcement learning tasks, CAGrad achieves improved performance over prior state-of-the-art multi-objective gradient manipulation methods. Code is available at https://github.com/Cranial-XIX/CAGrad. |
| Researcher Affiliation | Collaboration | Bo Liu, Xingchao Liu, Xiaojie Jin, , Peter Stone, Qiang Liu The University of Texas at Austin, Sony AI, Bytedance Research {bliu,xcliu,pstone,lqiang}@cs.utexas.edu, xjjin0731@gmail.com |
| Pseudocode | Yes | Algorithm 1 Conflict-averse Gradient Descent (CAGrad) for Multi-task Learning |
| Open Source Code | Yes | Code is available at https://github.com/Cranial-XIX/CAGrad. |
| Open Datasets | Yes | To answer questions (1) and (2), we create a toy optimization example to evaluate the convergence of CAGrad compared to MGDA and PCGrad. On the same toy example, we ablate over the constant c and show that CAGrad recovers GD and MGDA with proper c values. Next, to test CAGrad on more complicated neural models, we perform the same set of experiments on the Multi-Fashion+MNIST benchmark [19] with a shrinked Le Net architecture [18] (in which each layer has a reduced number of neurons compared to the original Le Net). Please refer to Appendix B for more details. |
| Dataset Splits | Yes | 10% of the training images is held out as the validation set. |
| Hardware Specification | Yes | All experiments are run on a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions software like Adam optimizer and Soft Actor-Critic (SAC) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We consider a shrinked Le Net as our model, and train it with Adam [16] optimizer with a 0.001 learning rate for 50 epochs using a batch size of 256. |