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Hierarchical Optimization-Derived Learning
Risheng Liu, Xuan Liu, Shangzhi Zeng +2
In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been pr…
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
Risheng Liu, Xuan Liu, Shangzhi Zeng +2
Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization. However, p…
Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application
Risheng Liu, Jiaxin Gao, Xuan Liu +1
In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs). Unfortunately,…
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond
Risheng Liu, Yaohua Liu, Shangzhi Zeng +1
In recent years, Bi-Level Optimization (BLO) techniques have received extensive attentions from both learning and vision communities. A variety of BLO models in complex and practic…
Value-Function-based Sequential Minimization for Bi-level Optimization
Risheng Liu, Xuan Liu, Shangzhi Zeng +2
Gradient-based Bi-Level Optimization (BLO) methods have been widely applied to handle modern learning tasks. However, most existing strategies are theoretically designed based on r…
A General Descent Aggregation Framework for Gradient-based Bi-level Optimization
Risheng Liu, Pan Mu, Xiaoming Yuan +2
In recent years, a variety of gradient-based methods have been developed to solve Bi-Level Optimization (BLO) problems in machine learning and computer vision areas. However, the t…
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond
Risheng Liu, Jiaxin Gao, Jin Zhang +2
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchi…
BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning
Yaohua Liu, Risheng Liu
Meta-learning (a.k.a. learning to learn) has recently emerged as a promising paradigm for a variety of applications. There are now many meta-learning methods, each focusing on diff…
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Risheng Liu, Pan Mu, Xiaoming Yuan +2
In recent years, a variety of gradient-based first-order methods have been developed to solve bi-level optimization problems for learning applications. However, theoretical guarant…
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
Jun Wen, Risheng Liu, Nenggan Zheng +3
Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation…
Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-convex Optimization
Yiyang Wang, Risheng Liu, Long Ma +1
We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex proble…