asymptotic optimality
Asymptotic optimality describes the behavior of an algorithm as the size of the input approaches infinity, indicating whether the algorithm achieves optimal performance in terms of efficiency and accuracy for large-scale problems.
- Impartial Selection with Predictions
- Joint‑Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self‑Supervised Learning
- Optimal Estimation of the Best Mean in Multi-Armed Bandits
- Pareto Optimal Risk-Agnostic Distributional Bandits with Heavy-Tail Rewards
- Projection-based Lyapunov method for fully heterogeneous weakly-coupled MDPs