Boosting Multitask Learning on Graphs through Higher-Order Task Affinities

Główne pojęcia

Efficiently clustering tasks based on higher-order task affinities improves multitask learning performance.

Streszczenie

This paper explores boosting multitask learning on graphs by clustering tasks into groups based on higher-order task affinities. The authors address the challenge of negative transfer in multitask learning, especially in complex overlapping community detection scenarios. They propose an algorithm to efficiently estimate task affinity scores and use spectral clustering to group tasks for improved performance. Experimental results show significant improvements over existing methods in community detection and molecular graph prediction datasets. The approach is stable across different settings and provides a promising direction for future work.

Statystyki

We validate our procedure using various community detection and molecular graph prediction data sets, showing favorable results compared with existing methods.
Our method can be viewed as a boosting procedure and can be used on top of any graph learning algorithm.
We estimate the task affinity scores through a sampling approach, which only requires fitting 𝑂(𝑇) MTL models.
Our approach requires training 𝑛 networks, one for each random subset, with 𝑛 ≤ 20𝑇 samples sufficing for estimating the task affinity scores to convergence.
Compared with previous methods, our approach requires 3.7× less running time, averaged over the four data sets.

Cytaty

"We observe that both positive and negative transfers appear in all four settings."
"Our results show that negative transfer occurs during MTL on graphs."
"Structural differences can cause negative interference in multitask learning."
"Our method can be viewed as a boosting procedure and can be used on top of any graph learning algorithm."

Kluczowe wnioski z

Boosting Multitask Learning on Graphs through Higher-Order Task Affinities

by Dongyue Li,H... o arxiv.org 03-18-2024

https://arxiv.org/pdf/2306.14009.pdf

Głębsze pytania

How does the proposed method compare to traditional heuristics for modeling task relatedness

The proposed method for modeling task relatedness through higher-order task affinities offers a more nuanced and effective approach compared to traditional heuristics. While traditional methods often rely on simplistic measures or theoretical perspectives that may not directly apply to nonlinear neural networks, the proposed method leverages the prediction loss of one task in the presence of another task and random subsets to estimate higher-order relationships accurately. By clustering tasks into groups based on these affinity scores, the proposed method optimizes multitask model performance by considering complex transfer relationships among tasks. This contrasts with heuristic-based approaches that may overlook such intricate inter-task dynamics.

What implications does the observed negative transfer have for practical applications of multitask learning

The observed negative transfer phenomenon in multitask learning has significant implications for practical applications across various domains. Negative transfer occurs when combining multiple tasks within a shared model leads to decreased performance compared to training each task individually (STL). In real-world scenarios like community detection or molecular graph prediction, negative transfers can hinder overall predictive accuracy and impede the benefits of multitask learning. Understanding and mitigating negative transfers are crucial for ensuring the effectiveness of MTL models in practice. The proposed boosting procedure through higher-order task affinities addresses this challenge by identifying optimal task groupings that minimize negative transfers and enhance overall performance.

How might the concept of higher-order task affinities extend to other domains beyond graph-based tasks

The concept of higher-order task affinities introduced in this study has broad implications beyond graph-based tasks, extending to various domains where multitask learning is applied. In fields like natural language processing, computer vision, healthcare analytics, and financial forecasting, where multiple related prediction tasks exist simultaneously, understanding complex inter-task relationships is essential for improving model performance. By incorporating higher-order affinity measures into MTL frameworks tailored to specific domains, practitioners can optimize predictive accuracy across diverse sets of tasks while minimizing negative transfers effectively. This approach opens up opportunities for enhancing MTL applications in numerous industries requiring simultaneous predictions from interconnected data sources or modalities.