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..
Efficient Continuous Pareto Exploration in Multi-Task Learning
Authors: Pingchuan Ma, Tao Du, Wojciech Matusik
ICML 2020 | Venue PDF | 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 <EMAIL>. |
| 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. |