optimization problem
A mathematical formulation in which the goal is to find the best solution under a given set of constraints, fundamental to training machine learning models.
- 3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization
- A Closed-Form Solution for Fast and Reliable Adaptive Testing
- Adaptive Discretization for Consistency Models
- Approximate Gradient Coding for Distributed Learning with Heterogeneous Stragglers
- Competitive Advantage Attacks to Decentralized Federated Learning
- ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks
- Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models
- Discovering Opinion Intervals from Conflicts in Signed Graphs
- Discovering Opinion Intervals from Conflicts in Signed Graphs
- Explainable Reinforcement Learning from Human Feedback to Improve Alignment
- Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
- Globally Optimal Policy Gradient Algorithms for Reinforcement Learning with PID Control Policies
- Gradient-Guided Epsilon Constraint Method for Online Continual Learning
- Learning (Approximately) Equivariant Networks via Constrained Optimization
- Learning-Augmented Online Bidding in Stochastic Settings
- MIBP-Cert: Certified Training against Data Perturbations with Mixed-Integer Bilinear Programs
- Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
- Obliviator Reveals the Cost of Nonlinear Guardedness in Concept Erasure
- Poison as Cure: Visual Noise for Mitigating Object Hallucinations in LVMs
- Predictability Enables Parallelization of Nonlinear State Space Models
- PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
- SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes
- Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function
- Solver-Free Decision-Focused Learning for Linear Optimization Problems
- Spike-timing-dependent Hebbian learning as noisy gradient descent
- Taught Well Learned Ill: Towards Distillation-conditional Backdoor Attack