Efficient Continuous Pareto Exploration in Multi-Task Learning
Authors: Pingchuan Ma, Tao Du, Wojciech Matusik
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our method on fve datasets with various size and model complexity, ranging from Mul ti MNIST (Sabour et al., 2017) that consists of 60k images and requires a network classifer with only 20k parameters, to UTKFace (Zhang et al., 2017), an image dataset with 3 objectives and a modern network structure with millions of parameters. |
| Researcher Affiliation | Academia | Pingchuan Ma * 1 Tao Du * 1 Wojciech Matusik 1 1MIT CSAIL. Correspondence to: Pingchuan Ma <pcma@csail.mit.edu>. |
| Pseudocode | Yes | Algorithm 1 Effcient Pareto Set Exploration |
| Open Source Code | Yes | The code and data are available online1. 1https://github.com/mit-gfx/Continuous Pareto MTL |
| Open Datasets | Yes | Multi MNIST (Sabour et al., 2017) and its two variants Fashion MNIST (Xiao et al., 2017) and Multi Fashion M- NIST, which are medium-sized datasets with two classi fcation tasks; 2) UCI Census-Income (Kohavi, 1996), a medium-sized demographic dataset with three binary pre diction tasks; 3) UTKFace (Zhang et al., 2017), a large dataset of face images. |
| Dataset Splits | No | The paper mentions using well-known datasets and a subset of Multi MNIST, but does not explicitly state the specific training, validation, or test data splits (e.g., percentages or exact counts) within the main body of the paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the minimal residual method (MINRES) but does not provide specific version numbers for software dependencies or libraries used for implementation (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We discussed in more detail two crucial hyperparameters (k and s) and reported the ablation study in Section 6. We started with a random Pareto station ary point x returned by Pareto MTL, followed by running Algorithm 1 with fxed parameters K = 1 and N = 5 on Multi MNIST and its two variants. |