# Bilevel Optimization

111 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

# Adaptive Personalized Federated Learning

Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize.

# OptNet: Differentiable Optimization as a Layer in Neural Networks

This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks.

# Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions

Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems.

# Second-Order Sensitivity Analysis for Bilevel Optimization

Many existing approaches to bilevel optimization employ first-order sensitivity analysis, based on the implicit function theorem (IFT), for the lower problem to derive a gradient of the lower problem solution with respect to its parameters; this IFT gradient is then used in a first-order optimization method for the upper problem.

# Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning

We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems.

# Spectral Inference Networks: Unifying Deep and Spectral Learning

We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.

# Truncated Back-propagation for Bilevel Optimization

Bilevel optimization has been recently revisited for designing and analyzing algorithms in hyperparameter tuning and meta learning tasks.

# Penalty Method for Inversion-Free Deep Bilevel Optimization

We present results on data denoising, few-shot learning, and training-data poisoning problems in a large-scale setting.

# MetaPoison: Practical General-purpose Clean-label Data Poisoning

Existing attacks for data poisoning neural networks have relied on hand-crafted heuristics, because solving the poisoning problem directly via bilevel optimization is generally thought of as intractable for deep models.

# Learning Data Augmentation with Online Bilevel Optimization for Image Classification

Data augmentation is a key practice in machine learning for improving generalization performance.