Bilevel Optimization
93 papers with code • 0 benchmarks • 0 datasets
Bilevel Optimization is a branch of optimization, which contains a nested optimization problem within the constraints of the outer optimization problem. The outer optimization task is usually referred as the upper level task, and the nested inner optimization task is referred as the lower level task. The lower level problem appears as a constraint, such that only an optimal solution to the lower level optimization problem is a possible feasible candidate to the upper level optimization problem.
Source: Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization
Benchmarks
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Most implemented papers
BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning
learning to learn) has recently emerged as a promising paradigm for a variety of applications.
Bilevel Optimization: Convergence Analysis and Enhanced Design
For the AID-based method, we orderwisely improve the previous convergence rate analysis due to a more practical parameter selection as well as a warm start strategy, and for the ITD-based method we establish the first theoretical convergence rate.
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing
Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker.
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start
We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction map.
Single-level Adversarial Data Synthesis based on Neural Tangent Kernels
In this paper, we propose a new generative model called the generative adversarial NTK (GA-NTK) that has a single-level objective.
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
We address a key challenge for neuro-symbolic (NeSy) systems by leveraging convex and bilevel optimization techniques to develop a general gradient-based framework for end-to-end neural and symbolic parameter learning.
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem.
Discriminatively Learned Hierarchical Rank Pooling Networks
First, we present "discriminative rank pooling" in which the shared weights of our video representation and the parameters of the action classifiers are estimated jointly for a given training dataset of labelled vector sequences using a bilevel optimization formulation of the learning problem.
SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset.
Deep Bilevel Learning
Our approach is based on the principles of cross-validation, where a validation set is used to limit the model overfitting.