42 citations · 107 across the 12 of their papers we have counts for
7 papers · 1 filter
Calibration tests beyond classification
David Widmann, Fredrik Lindsten, Dave Zachariah
Most supervised machine learning tasks are subject to irreducible prediction errors. Probabilistic predictive models address this limitation by providing probability distributions…
Learning Pareto-Efficient Decisions with Confidence
Sofia Ek, Dave Zachariah, Petre Stoica
The paper considers the problem of multi-objective decision support when outcomes are uncertain. We extend the concept of Pareto-efficient decisions to take into account the uncert…
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Muhammad Osama, Dave Zachariah, Petre Stoica
A spatial point process can be characterized by an intensity function which predicts the number of events that occur across space. In this paper, we develop a method to infer predi…
Robust Risk Minimization for Statistical Learning
Muhammad Osama, Dave Zachariah, Peter Stoica
We consider a general statistical learning problem where an unknown fraction of the training data is corrupted. We develop a robust learning method that only requires specifying an…
Reliable Semi-Supervised Learning when Labels are Missing at Random
Xiuming Liu, Dave Zachariah, Johan Wågberg +1
Semi-supervised learning methods are motivated by the availability of large datasets with unlabeled features in addition to labeled data. Unlabeled data is, however, not guaranteed…
Learning Localized Spatio-Temporal Models From Streaming Data
Muhammad Osama, Dave Zachariah, Thomas B. Schön
We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the trainin…