Bilevel Optimization
96 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
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Latest papers with no code
Functional Bilevel Optimization for Machine Learning
In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space.
Fully Zeroth-Order Bilevel Programming via Gaussian Smoothing
In this paper, we study and analyze zeroth-order stochastic approximation algorithms for solving bilvel problems, when neither the upper/lower objective values, nor their unbiased gradient estimates are available.
Whiteness-based bilevel learning of regularization parameters in imaging
We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussian noise.
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades.
Generalizing Reward Modeling for Out-of-Distribution Preference Learning
During meta-training, a bilevel optimization algorithm is utilized to learn a reward model capable of guiding policy learning to align with human preferences across various distributions.
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information
Physics informed neural networks (PINNs) have recently been proposed as surrogate models for solving process optimization problems.
An Accelerated Gradient Method for Simple Bilevel Optimization with Convex Lower-level Problem
In this paper, we focus on simple bilevel optimization problems, where we minimize a convex smooth objective function over the optimal solution set of another convex smooth constrained optimization problem.
On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
We study the complexity of finding stationary points with such an $y^*$-aware oracle: we propose a simple first-order method that converges to an $\epsilon$ stationary point using $O(\epsilon^{-6}), O(\epsilon^{-4})$ access to first-order $y^*$-aware oracles.
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
But bilevel problems such as incentive design, inverse reinforcement learning (RL), and RL from human feedback (RLHF) are often modeled as dynamic objective functions that go beyond the simple static objective structures, which pose significant challenges of using existing bilevel solutions.
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions.
