Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach
Authors: Heshan Devaka Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Tianyi Chen
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations on supervised and reinforcement learning demonstrate the effectiveness of our method relative to state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1Rensselaer Polytechnic Institute 2IBM Thomas J. Watson Research Center |
| Pseudocode | Yes | Algorithm 1 Mo Co: Stochastic Multi-objective gradient with Correction |
| Open Source Code | No | The paper mentions using a third-party codebase (MTRL codebase) but does not provide specific links or explicit statements for its own implementation of the described methodology. |
| Open Datasets | Yes | NYU-v2 (Silberman et al., 2012) and City Scapes (Cordts et al., 2015) datasets. ... Office-31 (Saenko et al., 2010) and Office-home(Venkateswara et al., 2017) datasets. ... Met-world environment (Yu et al., 2020b). |
| Dataset Splits | Yes | As per the implementation in (Lin & Zhang, 2022), 60% of the total dataset is used for training, 20% for validation, and the rest 20% for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models. It only mentions funding sources. |
| Software Dependencies | No | The paper mentions software components like 'MTRL codebase', 'soft actor-critic (SAC)', 'Adam', 'Seg Net', 'MTAN', and 'Res Net18' but does not specify their version numbers. |
| Experiment Setup | Yes | For all the algorithms, we use the initial learning rate of 0.001, exponentially decaying at a rate of 0.05. In this example for Mo Co, we use βk = 5/k0.5, where k is the number of iterations. ... All the MTL methods in comparison are trained for 200 epochs, using a batch size of 8. We use Adam as the optimizer with a learning rate of 0.0001 for the first 100 epochs, and with a learning rate of 0.00005 for the rest of the epochs. ... All the methods are trained for 2 million steps with a batch size of 1280. ... Table 10: Summary of hyper-parameter choices for Mo Co in each experiment. |