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.
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 | Venue PDF | LLM Run Details | Input Tokens: 36,742 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,416 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 | 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 Îēk = 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. |