# Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fields

arXiv: https://arxiv.org/abs/2406.18311 · HTML: https://mlsanity.com/paper/2406.18311/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding](https://mlsanity.com/paper/2312.08733/) (2023)
- [Disinformation Detection: An Evolving Challenge in the Age of LLMs](https://mlsanity.com/paper/2309.15847/) (2023)
- [MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning](https://mlsanity.com/paper/2312.08636/) (2023)
- [GAIA: A Transfer Learning System of Object Detection that Fits Your Needs](https://mlsanity.com/paper/2106.11346/) (2021)
- [A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation](https://mlsanity.com/paper/2404.13812/) (2024)
- [Rumor Detection with a novel graph neural network approach](https://mlsanity.com/paper/2403.16206/) (2024)
- [Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2404.17379/) (2024)
- [Image Captioning in news report scenario](https://mlsanity.com/paper/2403.16209/) (2024)
- [Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Poses](https://mlsanity.com/paper/2404.17685/) (2024)
- [Research on Image Recognition Technology Based on Multimodal Deep Learning](https://mlsanity.com/paper/2405.03091/) (2024)
- [Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance](https://mlsanity.com/paper/1810.06208/) (2018)
- [Beyond Bounding Box: Multimodal Knowledge Learning for Object Detection](https://mlsanity.com/paper/2205.04072/) (2022)
- [Visual Encoding and Debiasing for CTR Prediction](https://mlsanity.com/paper/2205.04168/) (2022)

## Built on by (most cited first, 8 of 8)

- [Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation](https://mlsanity.com/paper/2409.11703/) (2024)
- [Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments](https://mlsanity.com/paper/2409.14972/) (2024)
- [AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Way](https://mlsanity.com/paper/2409.14577/) (2024)
- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminates](https://mlsanity.com/paper/2410.15612/) (2024)
- [Comprehensive Overview of Artificial Intelligence Applications in Modern Industries](https://mlsanity.com/paper/2409.13059/) (2024)
- [Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace](https://mlsanity.com/paper/2409.17359/) (2024)
- [Enhanced Credit Score Prediction Using Ensemble Deep Learning Model](https://mlsanity.com/paper/2410.00256/) (2024)

## Abstract (arXiv)

This paper examines an online multi-task learning (OMTL) method, which processes data sequentially to predict labels across related tasks. The framework learns task weights and their relatedness concurrently. Unlike previous models that assumed static task relatedness, our approach treats tasks as initially independent, updating their relatedness iteratively using newly calculated weight vectors. We introduced three rules to update the task relatedness matrix: OMTLCOV, OMTLLOG, and OMTLVON, and compared them against a conventional method (CMTL) that uses a fixed relatedness value. Performance evaluations on three datasets a spam dataset and two EEG datasets from construction workers under varying conditions demonstrated that our OMTL methods outperform CMTL, improving accuracy by 1% to 3% on EEG data, and maintaining low error rates around 12% on the spam dataset.