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..
Fair Resource Allocation in Multi-Task Learning
Authors: Hao Ban, Kaiyi Ji
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method can achieve state-of-the-art performance among gradient manipulation methods on a suite of multitask benchmarks in supervised learning and reinforcement learning. Furthermore, we incorporate the idea of α-fairness into the loss functions of various MTL methods. Extensive empirical studies demonstrate that their performance can be significantly enhanced. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, University at Buffalo, New York, United States. Correspondence to: Kaiyi Ji <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Fair Grad for MTL |
| Open Source Code | Yes | Code is available at https: //github.com/Opt MN-Lab/fairgrad. |
| Open Datasets | Yes | Celeb A (Liu et al., 2015) is a large-scale face attributes dataset...QM9 (Ramakrishnan et al., 2014) is a widely-used benchmark...NYU-v2 (Silberman et al., 2012) contains...Cityscapes (Cordts et al., 2016) contains... |
| Dataset Splits | Yes | Following (Navon et al., 2022; Liu et al., 2023), we use the example provided in Pytorch Geometric (Fey & Lenssen, 2019), and use 110k molecules for training, 10k for validation, and the rest 10k for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions software like Pytorch Geometric, Soft Actor-Critic (SAC), and MTRL codebase, but does not provide specific version numbers for these software dependencies, which are necessary for reproducible descriptions. |
| Experiment Setup | Yes | We train our method for 15 epochs, using Adam optimizer with learning rate 3e-4. The batch size is 256. |