Stopping Bayesian Optimization with Probabilistic Regret Bounds

Nov-20-2025, 02:18:51 GMT–Neural Information Processing Systems

Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.

artificial intelligence, data mining, machine learning, (19 more...)

Neural Information Processing Systems

Nov-20-2025, 02:18:51 GMT

Conferences PDF

Country:
- Asia
  - Japan (0.04)
  - Russia > Siberian Federal District
    - Novosibirsk Oblast > Novosibirsk (0.04)
  - Uzbekistan > Toshkent Shahri
    - Tashkent (0.04)
- Europe
  - Italy > Friuli Venezia Giulia
    - Trieste Province > Trieste (0.04)
  - Russia (0.04)
  - United Kingdom > England
    - Cambridgeshire > Cambridge (0.04)
- North America > United States (0.04)

Genre:
- Research Report > Experimental Study (0.93)

Technology:
- Information Technology
  - Artificial Intelligence
    - Machine Learning
      - Learning Graphical Models > Directed Networks
        Bayesian Learning (0.67)
      - Neural Networks (1.00)
      - Performance Analysis > Accuracy (0.46)
      - Statistical Learning (1.00)
    - Representation & Reasoning
      - Optimization (0.68)
      - Uncertainty > Bayesian Inference (0.67)
  - Data Science > Data Mining (0.87)

Duplicate Docs Excel Report

Title
Stopping Bayesian Optimization with Probabilistic Regret Bounds

Similar Docs Excel Report more

Title	Similarity	Source
None found