Papers Bilevel Optimization
“Bilevel Optimization” 태그가 달린 논문 544편 · 필터 해제
Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization
To overcome the limitations of classical partially connected Boltzmann machines and mainstream quantum Boltzmann machines (QBMs), this work extends the conventional circuit of the quantum approximate optimization algorit…
Bilevel OptimizationImage GenerationPenalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems
We study a class of bilevel optimization problems in which both the upper- and lower-level problems have minimax structures. This setting captures a broad range of emerging applications. Despite the extensive literature …
Bilevel OptimizationReActor: Reinforcement Learning for Physics-Aware Motion Retargeting
Retargeting human kinematic reference motion onto a robot's morphology remains a formidable challenge. Existing methods often produce physical inconsistencies, such as foot sliding, self-collisions, or dynamically infeas…
Reinforcement LearningBilevel OptimizationCausal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings
We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained triple-head price optimization (C3PO) network to solve a bilevel deci…
Bilevel OptimizationMulti-Task LearningDecision MakingNoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training
Diffusion models have achieved remarkable success across a wide range of generative tasks, yet their training paradigm largely treats injected noise as uniformly informative. In this work, we challenge this assumption an…
Bilevel OptimizationAI Alignment via Incentives and Correction
We study AI alignment through the lens of law-and-economics models of deterrence and enforcement. In these models, misconduct is not treated as an external failure, but as a strategic response to incentives: an actor wei…
Bilevel OptimizationSafe Bilevel Delegation (SBD): A Formal Framework for Runtime Delegation Safety in Multi-Agent Systems
As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents becomes critical. Existing work addresses multi-agent architecture …
Bilevel OptimizationFlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients
LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle diverse tasks. However, existing approa…
Bilevel OptimizationAdversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective
Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial training, often fail against adaptive attackers who use reinforcement learn…
Reinforcement LearningBilevel OptimizationMalware DetectionMeta Additive Model: Interpretable Sparse Learning With Auto Weighting
Sparse additive models have attracted much attention in high-dimensional data analysis due to their flexible representation and strong interpretability. However, most existing models are limited to single-level learning …
Bilevel OptimizationSparse LearningOn the Stability and Generalization of First-order Bilevel Minimax Optimization
Bilevel optimization and bilevel minimax optimization have recently emerged as unifying frameworks for a range of machine-learning tasks, including hyperparameter optimization and reinforcement learning. The existing lit…
Hyperparameter OptimizationReinforcement LearningBilevel OptimizationS2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
Semi-supervised learning with manifold regularization is a classical framework for jointly learning from both labeled and unlabeled data, where the key requirement is that the support of the unknown marginal distribution…
Bilevel OptimizationBilevel Optimization of Agent Skills via Monte Carlo Tree Search
Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. Empirical evidence shows that the design …
Bilevel OptimizationQuestion AnsweringBilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem
This paper tackles the Electric Capacitated Vehicle Routing Problem (E-CVRP) through a bilevel optimization framework that handles routing and charging decisions separately or jointly depending on the search stage. By an…
Bilevel OptimizationMulti-Agent Decision-Focused Learning via Value-Aware Sequential Communication
Multi-agent coordination under partial observability requires agents to share complementary private information. While recent methods optimize messages for intermediate objectives (e.g., reconstruction accuracy or mutual…
Bilevel OptimizationVertAX: a differentiable vertex model for learning epithelial tissue mechanics
Epithelial tissues dynamically reshape through local mechanical interactions among cells, a process well captured by vertex models. Yet their many tunable parameters make inference and optimization challenging, motivatin…
Bilevel OptimizationFine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization
Stochastic bilevel optimization (SBO) has been integrated into many machine learning paradigms recently, including hyperparameter optimization, meta learning, and reinforcement learning. Along with the wide range of appl…
Hyperparameter OptimizationReinforcement LearningBilevel OptimizationAccelerating Black-Box Bilevel Optimization with Rank-Based Upper-Level Value Function Approximation
Bilevel optimization is a field of significant theoretical and practical interest, yet solving such optimization problems remains challenging. Evolutionary methods have been employed to address these problems in the blac…
Bilevel OptimizationEfficient Bilevel Optimization with KFAC-Based Hypergradients
Bilevel optimization (BO) is widely applicable to many machine learning problems. Scaling BO, however, requires repeatedly computing hypergradients, which involves solving inverse Hessian-vector products (IHVPs). In prac…
Bilevel OptimizationEfficient and Versatile Quadrupedal Skating: Optimal Co-design via Reinforcement Learning and Bayesian Optimization
In this paper, we present a hardware-control co-design approach that enables efficient and versatile roller skating on quadrupedal robots equipped with passive wheels. Passive-wheel skating reduces leg inertia and improv…
Reinforcement LearningBilevel Optimization