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High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling
We study the problem of performing automated experiment design for drug screening through Bayesian inference and optimisation.
Retrain or not retrain: Conformal test martingales for change-point detection
We argue for supplementing the process of training a prediction algorithm by setting up a scheme for detecting the moment when the distribution of the data changes and the algorithm needs to be retrained.
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets
Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm.
Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction
Conformal Prediction is a machine learning methodology that produces valid prediction regions under mild conditions.
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery
This paper explores conformal prediction in the learning under privileged information (LUPI) paradigm.
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