Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture
Models
- URL: http://arxiv.org/abs/2209.15224v2
- Date: Thu, 28 Dec 2023 14:33:13 GMT
- Title: Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture
Models
- Authors: Ye Tian, Haolei Weng, Yang Feng
- Abstract summary: We study the multi-task learning problem on GMMs.
We propose a multi-task GMM learning procedure based on the EM algorithm.
We generalize our approach to tackle the problem of transfer learning for GMMs.
- Score: 15.574915079821473
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised learning has been widely used in many real-world applications.
One of the simplest and most important unsupervised learning models is the
Gaussian mixture model (GMM). In this work, we study the multi-task learning
problem on GMMs, which aims to leverage potentially similar GMM parameter
structures among tasks to obtain improved learning performance compared to
single-task learning. We propose a multi-task GMM learning procedure based on
the EM algorithm that not only can effectively utilize unknown similarity
between related tasks but is also robust against a fraction of outlier tasks
from arbitrary distributions. The proposed procedure is shown to achieve
minimax optimal rate of convergence for both parameter estimation error and the
excess mis-clustering error, in a wide range of regimes. Moreover, we
generalize our approach to tackle the problem of transfer learning for GMMs,
where similar theoretical results are derived. Finally, we demonstrate the
effectiveness of our methods through simulations and real data examples. To the
best of our knowledge, this is the first work studying multi-task and transfer
learning on GMMs with theoretical guarantees.
Related papers
- The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrations [13.747258771184372]
There are several open challenges to applying TP-GMMs in the wild.
We factorize the robot's end-effector velocity into its direction and magnitude.
We then segment and sequence skills from complex demonstration trajectories.
Our approach enables learning complex manipulation tasks from just five demonstrations.
arXiv Detail & Related papers (2024-07-18T12:01:09Z) - MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic [6.46176287368784]
We propose textbfModel textbfExclusive textbfTask textbfArithmetic for merging textbfGPT-scale models.
Our proposed MetaGPT is data-agnostic and bypasses the heavy search process, making it cost-effective and easy to implement for LLMs.
arXiv Detail & Related papers (2024-06-17T10:12:45Z) - Sample Efficient Myopic Exploration Through Multitask Reinforcement
Learning with Diverse Tasks [53.44714413181162]
This paper shows that when an agent is trained on a sufficiently diverse set of tasks, a generic policy-sharing algorithm with myopic exploration design can be sample-efficient.
To the best of our knowledge, this is the first theoretical demonstration of the "exploration benefits" of MTRL.
arXiv Detail & Related papers (2024-03-03T22:57:44Z) - AdaMerging: Adaptive Model Merging for Multi-Task Learning [68.75885518081357]
This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging)
It aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data.
Compared to the current state-of-the-art task arithmetic merging scheme, AdaMerging showcases a remarkable 11% improvement in performance.
arXiv Detail & Related papers (2023-10-04T04:26:33Z) - Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language
Transfer Learning [59.38343286807997]
We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks.
Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients.
We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
arXiv Detail & Related papers (2022-03-09T17:26:53Z) - Multi-Task Learning as a Bargaining Game [63.49888996291245]
In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for several tasks.
Since the gradients of these different tasks may conflict, training a joint model for MTL often yields lower performance than its corresponding single-task counterparts.
We propose viewing the gradients combination step as a bargaining game, where tasks negotiate to reach an agreement on a joint direction of parameter update.
arXiv Detail & Related papers (2022-02-02T13:21:53Z) - Meta-Reinforcement Learning in Broad and Non-Parametric Environments [8.091658684517103]
We introduce TIGR, a Task-Inference-based meta-RL algorithm for tasks in non-parametric environments.
We decouple the policy training from the task-inference learning and efficiently train the inference mechanism on the basis of an unsupervised reconstruction objective.
We provide a benchmark with qualitatively distinct tasks based on the half-cheetah environment and demonstrate the superior performance of TIGR compared to state-of-the-art meta-RL approaches.
arXiv Detail & Related papers (2021-08-08T19:32:44Z) - MAML is a Noisy Contrastive Learner [72.04430033118426]
Model-agnostic meta-learning (MAML) is one of the most popular and widely-adopted meta-learning algorithms nowadays.
We provide a new perspective to the working mechanism of MAML and discover that: MAML is analogous to a meta-learner using a supervised contrastive objective function.
We propose a simple but effective technique, zeroing trick, to alleviate such interference.
arXiv Detail & Related papers (2021-06-29T12:52:26Z) - Bridging Multi-Task Learning and Meta-Learning: Towards Efficient
Training and Effective Adaptation [19.792537914018933]
Multi-task learning (MTL) aims to improve the generalization of several related tasks by learning them jointly.
Modern meta-learning allows unseen tasks with limited labels during the test phase, in the hope of fast adaptation over them.
We show that MTL shares the same optimization formulation with a class of gradient-based meta-learning (GBML) algorithms.
arXiv Detail & Related papers (2021-06-16T17:58:23Z) - An unsupervised deep learning framework via integrated optimization of
representation learning and GMM-based modeling [31.334196673143257]
This paper introduces a new principle of joint learning on both deep representations and GMM-based deep modeling.
In comparison with the existing work in similar areas, our objective function has two learning targets, which are created to be jointly optimized.
The compactness of clusters is significantly enhanced by reducing the intra-cluster distances, and the separability is improved by increasing the inter-cluster distances.
arXiv Detail & Related papers (2020-09-11T04:57:03Z) - Task-Feature Collaborative Learning with Application to Personalized
Attribute Prediction [166.87111665908333]
We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL)
Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks.
As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
arXiv Detail & Related papers (2020-04-29T02:32:04Z)
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.