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..
Multi-Objective Deep Learning with Adaptive Reference Vectors
Authors: Weiyu Chen, James Kwok
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on an extensive set of learning scenarios demonstrate the superiority of the proposed algorithm over the state-of-the-art. 4 Experiments In this section, extensive experiments are performed, including synthetic problems (Section 4.1), multi-task learning (Section 4.2), accuracy-fairness trade-off (Section 4.3), and usage on larger networks (Section 4.4). Finally, ablation study is presented in Section 4.5. |
| Researcher Affiliation | Academia | Weiyu Chen James T. Kwok Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1 Gradient-based Multi-Objective Optimization with Adaptive Reference vectors (GMOOAR). |
| Open Source Code | No | The paper does not contain an explicit statement about open-sourcing its code or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | In this experiment, we use three benchmark datasets from [31]: Multi-MNIST, Multi-Fashion, and Multi-Fashion+MNIST. [...] we aim to achieve both high accuracy and fairness on three tabular datasets: Adult [16], Compass [1], and Default [51]. [...] selected from the 40 tasks in Celeb A [35]. |
| Dataset Splits | Yes | They are evaluated on the validation set every 5 epochs. We only keep the solutions of iteration kbest as the ο¬nal solution set, where kbest is the iteration that yields the solution set with the largest validation HV. |
| Hardware Specification | Yes | All experiments are conducted on an RTX-2080Ti with 11GB memory. |
| Software Dependencies | No | The paper mentions using a 'neural network' and 'Le Net' but does not specify any software frameworks (like PyTorch, TensorFlow) or their version numbers, nor any other libraries with versions. |
| Experiment Setup | Yes | As in [40], a neural network (with 2 hidden layers, each with 20 units) is used. Following common practice [44], we obtain a set of n solutions in each iteration (with n = 15 in all experiments). For EPO, PHN-LS, PHN-EPO and COSMOS, we generate reference vectors following the strategy in [44]. For GMOOAR, the reference vectors are initialized randomly. |