Multi-Task Learning as a Bargaining Game
Authors: Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that Nash-MTL achieves state-of-the-art results on multiple MTL benchmarks in various domains. |
| Researcher Affiliation | Collaboration | 1Bar-Ilan University, Ramat Gan, Israel 2Nvidia, Tel-Aviv, Israel 3National University of Singapore. |
| Pseudocode | Yes | Algorithm 1 Nash-MTL |
| Open Source Code | Yes | To support future research and the reproducibility of the results, we make our source code publicly available at: https://github.com/Aviv Navon/nash-mtl. |
| Open Datasets | Yes | We evaluate Nash-MTL on predicting 11 properties of molecules from the QM9 dataset (Ramakrishnan et al., 2014)... We use 110K molecules for training, 10K for validation, and 10K as a test set. We follow the protocol of (Liu et al., 2019b) and evaluate Nash-MTL on the NYUv2 and Cityscapes datasets (Silberman et al., 2012; Cordts et al., 2016)... We consider a multi-task RL problem and evaluate Nash MTL on the MT10 environment from the Meta-World benchmark (Yu et al., 2020b). |
| Dataset Splits | Yes | We use 110K molecules for training, 10K for validation, and 10K as a test set. |
| Hardware Specification | No | The paper does not explicitly state the specific hardware used (e.g., GPU models, CPU types) for running its experiments. |
| Software Dependencies | No | The paper mentions using Py Torch Geometric, Seg Net architecture, and Soft Actor-Critic (SAC) but does not provide specific version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | We train each method for 300 epochs and search for the best learning-rate (lr)... Each method is trained for 200 epochs with the Adam optimizer (Kingma & Ba, 2015) and an initial learning-rate of 1e 4. The learning-rate is halved to 5e 5 after 100 epochs. ... We use a batch size of 2 and 8 for NYUv2 and City Scapes respectively. ... Each method is trained over 2 million steps with a batch size of 1280. |