Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit
- URL: http://arxiv.org/abs/2409.02708v1
- Date: Wed, 4 Sep 2024 13:44:22 GMT
- Title: Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit
- Authors: Chaozhi Zhang, Lin Liu, Xiaoqun Zhang,
- Abstract summary: We propose a new algorithm called Meta Subspace Pursuit (abbreviated as Meta-SP)
Under this assumption, we propose a new algorithm, called Meta Subspace Pursuit (abbreviated as Meta-SP)
- Score: 9.421309916099428
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the availability of big datasets. One effective strategy to mitigate the issue of insufficient data is to first harness information from other data sources possessing certain similarities in the study design stage, and then employ the multi-task or meta learning framework in the analysis stage. In this paper, we focus on multi-task (or multi-source) linear models whose coefficients across tasks share an invariant low-rank component, a popular structural assumption considered in the recent multi-task or meta learning literature. Under this assumption, we propose a new algorithm, called Meta Subspace Pursuit (abbreviated as Meta-SP), that provably learns this invariant subspace shared by different tasks. Under this stylized setup for multi-task or meta learning, we establish both the algorithmic and statistical guarantees of the proposed method. Extensive numerical experiments are conducted, comparing Meta-SP against several competing methods, including popular, off-the-shelf model-agnostic meta learning algorithms such as ANIL. These experiments demonstrate that Meta-SP achieves superior performance over the competing methods in various aspects.
Related papers
- Multi-modal Semantic Understanding with Contrastive Cross-modal Feature
Alignment [11.897888221717245]
This paper proposes a novel CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment.
Our model is simple to implement without using task-specific external knowledge, and thus can easily migrate to other multi-modal tasks.
arXiv Detail & Related papers (2024-03-11T01:07:36Z) - Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement [69.51496713076253]
In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods.
We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground.
We also propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL.
arXiv Detail & Related papers (2024-02-05T22:15:55Z) - Concrete Subspace Learning based Interference Elimination for Multi-task
Model Fusion [86.6191592951269]
Merging models fine-tuned from common extensively pretrained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multitask model that performs well across diverse tasks.
We propose the CONtinuous relaxation dis (Concrete) subspace learning method to identify a common lowdimensional subspace and utilize its shared information track interference problem without sacrificing performance.
arXiv Detail & Related papers (2023-12-11T07:24:54Z) - Multi-Task Learning with Summary Statistics [4.871473117968554]
We propose a flexible multi-task learning framework utilizing summary statistics from various sources.
We also present an adaptive parameter selection approach based on a variant of Lepski's method.
This work offers a more flexible tool for training related models across various domains, with practical implications in genetic risk prediction.
arXiv Detail & Related papers (2023-07-05T15:55:23Z) - The Effect of Diversity in Meta-Learning [79.56118674435844]
Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples.
Recent studies show that task distribution plays a vital role in the model's performance.
We study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms.
arXiv Detail & Related papers (2022-01-27T19:39:07Z) - Multimodality in Meta-Learning: A Comprehensive Survey [34.69292359136745]
This survey provides a comprehensive overview of the multimodality-based meta-learning landscape.
We first formalize the definition of meta-learning and multimodality, along with the research challenges in this growing field.
We then propose a new taxonomy to systematically discuss typical meta-learning algorithms combined with multimodal tasks.
arXiv Detail & Related papers (2021-09-28T09:16:12Z) - Meta Navigator: Search for a Good Adaptation Policy for Few-shot
Learning [113.05118113697111]
Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data.
Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios.
We present Meta Navigator, a framework that attempts to solve the limitation in few-shot learning by seeking a higher-level strategy.
arXiv Detail & Related papers (2021-09-13T07:20:01Z) - Meta-Learning with Fewer Tasks through Task Interpolation [67.03769747726666]
Current meta-learning algorithms require a large number of meta-training tasks, which may not be accessible in real-world scenarios.
By meta-learning with task gradient (MLTI), our approach effectively generates additional tasks by randomly sampling a pair of tasks and interpolating the corresponding features and labels.
Empirically, in our experiments on eight datasets from diverse domains, we find that the proposed general MLTI framework is compatible with representative meta-learning algorithms and consistently outperforms other state-of-the-art strategies.
arXiv Detail & Related papers (2021-06-04T20:15:34Z) - Multi-Task Learning for Dense Prediction Tasks: A Survey [87.66280582034838]
Multi-task learning (MTL) techniques have shown promising results w.r.t. performance, computations and/or memory footprint.
We provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision.
arXiv Detail & Related papers (2020-04-28T09:15:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.