learning to optimize

A Hybrid Learning-to-Optimize Framework for Mixed-Integer Quadratic Programming L4DC 2026 Learning to Solve Constrained Bilevel Control Co-Design Problems L4DC 2026 From Learning to Optimize to Learning Optimization Algorithms AISTATS 2025 Learning to Generate Gradients for Test-Time Adaptation via Test-Time Training Layers AAAI 2025 Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation JMLR 2025 PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming ICML 2024 Towards Robust Learning to Optimize with Theoretical Guarantees CVPR 2024 Learning to Generalize Provably in Learning to Optimize AISTATS 2023 Learning to Optimize Differentiable Games ICML 2023 M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation ICLR 2023 PAC-Bayesian Learning of Optimization Algorithms AISTATS 2023 Towards Constituting Mathematical Structures for Learning to Optimize ICML 2023 Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How ICLR 2022 Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations IJCAI 2022 Learning to Optimize: A Primer and A Benchmark JMLR 2022 Optimizer Amalgamation ICLR 2022 Symbolic Learning to Optimize: Towards Interpretability and Scalability ICLR 2022 Learning A Minimax Optimizer: A Pilot Study ICLR 2021 Learning to Learn Graph Topologies NeurIPS 2021 Learning to Switch Optimizers for Quadratic Programming ACML 2021 Training Stronger Baselines for Learning to Optimize NeurIPS 2020 HyperAdam: A Learnable Task-Adaptive Adam for Network Training AAAI 2019 Learning to Optimize in Swarms NeurIPS 2019 Learning to Optimize Combinatorial Functions ICML 2018