Bilevel Optimization
97 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
These leaderboards are used to track progress in Bilevel Optimization
Most implemented papers
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
orlitany/SOSELETO
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ICLR 2019
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
sjenni/DeepBilevel
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ECCV 2018
Our approach is based on the principles of cross-validation, where a validation set is used to limit the model overfitting.
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation
chaoyanghe/MiLeNAS
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CVPR 2020
To remedy this, this paper proposes \mldas, a mixed-level reformulation for NAS that can be optimized efficiently and reliably.
Coresets via Bilevel Optimization for Continual Learning and Streaming
zalanborsos/bilevel_coresets
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NeurIPS 2020
Coresets are small data summaries that are sufficient for model training.
Inexact Derivative-Free Optimization for Bilevel Learning
A drawback of these techniques is that they are dependent on a number of parameters which have to be set by the user.
Learning to Play Sequential Games versus Unknown Opponents
We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action.
MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning
Kaushalya/metal
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In this paper, we propose MetAL, an AL approach that selects unlabeled instances that directly improve the future performance of a classification model.
Bilevel Continual Learning
phquang/Bilevel-Continual-Learning
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Continual learning aims to learn continuously from a stream of tasks and data in an online-learning fashion, being capable of exploiting what was learned previously to improve current and future tasks while still being able to perform well on the previous tasks.
Targeted Data-driven Regularization for Out-of-Distribution Generalization
mmkamani7/Targeted-Meta-Learning
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The proposed framework, named targeted data-driven regularization (TDR), is model- and dataset-agnostic and employs a target dataset that resembles the desired nature of test data in order to guide the learning process in a coupled manner.
