Search Results for author:
Found 6 papers, 2 papers with code
Bayesian Active Learning for Censored Regression
To address this, we derive the entropy and the mutual information for censored distributions and derive the BALD objective for active learning in censored regression ($\mathcal{C}$-BALD).
Applied metamodelling for ATM performance simulations
Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed.
Learning and Generalizing Polynomials in Simulation Metamodeling
The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are described by polynomials.
NOSTROMO: Lessons learned, conclusions and way forward
This White Paper sets out to explain the value that metamodelling can bring to air traffic management (ATM) research.
Bayesian Active Learning with Fully Bayesian Gaussian Processes
In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can cause inefficient and non-optimal querying, leading to unnecessary data labeling.
On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline
In this paper, we revisit the popularity prediction on Instagram.
