Papers Bilevel Optimization
“Bilevel Optimization” 태그가 달린 논문 544편 · 필터 해제
Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints
Task planning often suffers from severe efficiency bottlenecks when robots must reason over long-horizon action sequences under complex logical constraints, including object affordances, spatial relationships, and sequen…
Bilevel OptimizationPseudospectral Bounds for Transient Amplification in Coupled Gradient Descent
Coupled gradient descent - where the update of one parameter depends on another - arises naturally in bilevel optimization, two-time-scale stochastic approximation, and generative adversarial networks. When the coupled J…
Bilevel OptimizationRevisiting Zeroth-Order Hessian Approximation: A Single-Step Policy Optimization Lens
Accurate Zeroth-Order (ZO) Hessian estimation is a cornerstone of derivative-free methods, essential for tasks such as bilevel optimization, Bayesian inference, and uncertainty quantification. However, obtaining a comple…
Bilevel OptimizationBayesian InferenceS$^3$LDBO: A Snapshot Single-Loop Algorithm for Decentralized Bilevel Optimization
Networked AI systems increasingly rely on multiple agents that collaboratively learn and adapt models over communication networks. In such systems, bilevel formulations naturally arise in hyperparameter optimization, dat…
Hyperparameter OptimizationComputational EfficiencyBilevel OptimizationDensity-aware Sample-specific Attack
Despite recent progress in backdoor attacks, existing methods remain susceptible to post-training defenses that erase the backdoor through fine-tuning or pruning. We revisit the core objectives of backdoor attacks and de…
Bilevel OptimizationA Surveillance Evasion Game with Continuous Sensor Redeployment via Bilevel Optimization
Uncrewed Aerial Systems (UASs) have become a growing threat to the security of critical infrastructure, exploiting spatiotemporal gaps in sensor perimeters to infiltrate restricted airspace undetected. We formulate this …
Bilevel OptimizationBilevel Optimization over Saddle Points of Zero-Sum Markov Games
Reinforcement learning (RL) often has a hierarchical structure, where an upper-level (UL) learner selects model parameters and a lower-level (LL) decision-making process responds, naturally leading to a bilevel optimizat…
Reinforcement LearningBilevel OptimizationDetectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run
Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy (DP) parameters. Recent one-run auditin…
Bilevel OptimizationBilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning
While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a scalable approach, yet its performance hinges on the availability and qua…
Reinforcement LearningBilevel OptimizationSeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation
Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models, but composing multiple custom concepts remains challenging due to representation interference. Existing modular methods eith…
parameter-efficient fine-tuningBilevel OptimizationContinual LearningImage GenerationEfficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning
Training a deep neural network with noisy labels could reduce data annotation cost but may introduce noise into the learned model. In meta label correction approaches, an additional meta model besides the main model is t…
Bilevel OptimizationBalancing Knowledge Distillation for Imbalance Learning with Bilevel Optimization
Knowledge distillation transfers knowledge from a high capacity teacher to a compact student using a mixture of hard and soft losses. On imbalanced data, a fixed weighting between hard and soft losses becomes brittle the…
Knowledge DistillationBilevel OptimizationLearning with Semantic Priors: Stabilizing Point-Supervised Infrared Small Target Detection via Hierarchical Knowledge Distillation
Single-frame Infrared Small Target Detection (ISTD) aims to localize weak targets under heavy background clutter, yet dense pixel-wise annotations are expensive. Point supervision with online label evolution reduces anno…
Knowledge DistillationBilevel OptimizationA Barrier-Metric First-Order Method for Linearly Constrained Bilevel Optimization
We study bilevel optimization with a fixed polyhedral lower feasible set. Such problems are challenging for two reasons: active-set changes can make the upper objective nonsmooth, and existing hypergradient methods typic…
Bilevel OptimizationIGT-OMD: Implicit Gradient Transport for Decision-Focused Learning under Delayed Feedback
Decision-focused learning trains predictive models end-to-end against downstream decision loss, but online settings suffer delayed feedback: outcomes may not arrive for many environment interactions. We identify \emph{st…
Bilevel OptimizationFocuSFT: Bilevel Optimization for Dilution-Aware Long-Context Fine-Tuning
Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains limited. We trace this gap to how attention budget is spent during supe…
Bilevel OptimizationBROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization
Stochastic bilevel optimization (SBO) has become a standard framework for hyperparameter learning, data reweighting, representation learning, and data-mixture optimization in deep learning. Existing exact single-loop SBO…
Representation LearningBilevel OptimizationRobust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and communication efficiency, one-shot CML has been increasingly adopte…
Bilevel OptimizationA Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization
In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing works on BLO remain confined to the single-task setting and rely on t…
Bilevel OptimizationSelect-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
We study optimistic bilevel optimization when the lower-level problem has a non-isolated manifold of minimizers. In this setting, the hyper-objective may be non-differentiable because the upper-level criterion must choos…
Bilevel Optimization