optimization problems
Optimization problems in AI involve finding the best parameters or configurations for a model to minimize or maximize a given objective function, a central theme in machine learning training processes.
- ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
- Acceleration via silver step-size on Riemannian manifolds with applications to Wasserstein space
- Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees
- Constrained Linear Thompson Sampling
- Fast Computation and Optimization for Opinion-Based Quantities of Friedkin-Johnsen Model
- Follow-the-Perturbed-Leader Nearly Achieves Best-of-Both-Worlds for the m-Set Semi-Bandit Problems
- Individually Fair Diversity Maximization
- Quantum Speedups for Minimax Optimization and Beyond
- RoME: Domain-Robust Mixture-of-Experts for MILP Solution Prediction across Domains
- SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search
- Tight Bounds for Maximum Weight Matroid Independent Set and Matching in the Zero Communication Model