Efficient Pareto Manifold Learning with Low-Rank Structure

• URL: http://arxiv.org/abs/2407.20734v1
• Date: Tue, 30 Jul 2024 11:09:27 GMT
• Title: Efficient Pareto Manifold Learning with Low-Rank Structure
• Authors: Weiyu Chen, James T. Kwok,
• Abstract summary: Multi-task learning is inherently a multi-objective optimization problem. We propose a novel approach that integrates a main network with several low-rank matrices. It significantly reduces the number of parameters and facilitates the extraction of shared features.
• Score: 31.082432589391953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task learning, which optimizes performance across multiple tasks, is inherently a multi-objective optimization problem. Various algorithms are developed to provide discrete trade-off solutions on the Pareto front. Recently, continuous Pareto front approximations using a linear combination of base networks have emerged as a compelling strategy. However, it suffers from scalability issues when the number of tasks is large. To address this issue, we propose a novel approach that integrates a main network with several low-rank matrices to efficiently learn the Pareto manifold. It significantly reduces the number of parameters and facilitates the extraction of shared features. We also introduce orthogonal regularization to further bolster performance. Extensive experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines, especially on datasets with a large number of tasks.

Related papers

• Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion [53.33473557562837]
  Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost. We propose a practical and scalable approach to solve this problem via mixture of experts (MoE) based model fusion. By ensembling the weights of specialized single-task models, the MoE module can effectively capture the trade-offs between multiple objectives.
  arXiv  Detail & Related papers  (2024-06-14T07:16:18Z)
• UCB-driven Utility Function Search for Multi-objective Reinforcement Learning [75.11267478778295]
  In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours. We focus on the case of linear utility functions parameterised by weight vectors w. We introduce a method based on Upper Confidence Bound to efficiently search for the most promising weight vectors during different stages of the learning process.
  arXiv  Detail & Related papers  (2024-05-01T09:34:42Z)
• Multi-Task Learning with Multi-Task Optimization [31.518330903602095]
  We show that a set of optimized yet well-distributed models embody different trade-offs in one algorithmic pass. We investigate the proposed multi-task learning with multi-task optimization for solving various problem settings.
  arXiv  Detail & Related papers  (2024-03-24T14:04:40Z)
• Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models [50.33956216274694]
  In Multi-Task Learning (MTL), tasks may compete and limit the performance achieved on each other, rather than guiding the optimization to a solution. We propose textitPareto Manifold Learning, an ensembling method in weight space.
  arXiv  Detail & Related papers  (2022-10-18T11:20:54Z)
• Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set [17.617944390196286]
  Modern machine learning applications, such as multi-task learning, require finding optimal model parameters to trade-off multiple objective functions. We propose a first-order algorithm that approximately solves OPT-in-Pareto using only gradient information.
  arXiv  Detail & Related papers  (2021-10-17T04:07:04Z)
• Multi-task Supervised Learning via Cross-learning [102.64082402388192]
  We consider a problem known as multi-task learning, consisting of fitting a set of regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other. This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task.
  arXiv  Detail & Related papers  (2020-10-24T21:35:57Z)
• Small Towers Make Big Differences [59.243296878666285]
  Multi-task learning aims at solving multiple machine learning tasks at the same time. A good solution to a multi-task learning problem should be generalizable in addition to being Pareto optimal. We propose a method of under- parameterized self-auxiliaries for multi-task models to achieve the best of both worlds.
  arXiv  Detail & Related papers  (2020-08-13T10:45:31Z)
• Efficient Continuous Pareto Exploration in Multi-Task Learning [34.41682709915956]
  We present a novel, efficient method for continuous analysis of optimal solutions in machine learning problems. We scale up theoretical results in multi-objective optimization to modern machine learning problems by proposing a sample-based sparse linear system.
  arXiv  Detail & Related papers  (2020-06-29T23:36:20Z)
• Maximum Roaming Multi-Task Learning [18.69970611732082]
  We present a novel way to partition the parameter space without weakening the inductive bias. Specifically, we propose Maximum Roaming, a method inspired by dropout that randomly varies the parameter partitioning. Experimental results suggest that the regularization brought by roaming has more impact on performance than usual partitioning optimization strategies.
  arXiv  Detail & Related papers  (2020-06-17T10:25:41Z)
• Pareto Multi-Task Learning [53.90732663046125]
  Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. It is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.
  arXiv  Detail & Related papers  (2019-12-30T08:58:40Z)

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.