129 citations · 568 across the 49 of their papers we have counts for
17 papers · 1 filter
Certified Inventory Control of Critical Resources
Ludvig Hult, Dave Zachariah, Petre Stoica
Inventory control is subject to service-level requirements, in which sufficient stock levels must be maintained despite an unknown demand. We propose a data-driven order policy tha…
Off-Policy Evaluation with Out-of-Sample Guarantees
Sofia Ek, Dave Zachariah, Fredrik D. Johansson +1
We consider the problem of evaluating the performance of a decision policy using past observational data. The outcome of a policy is measured in terms of a loss (aka. disutility or…
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…
Diagnostic Tool for Out-of-Sample Model Evaluation
Ludvig Hult, Dave Zachariah, Petre Stoica
Assessment of model fitness is a key part of machine learning. The standard paradigm is to learn models by minimizing a chosen loss function averaged over training data, with the a…
Surprises in adversarially-trained linear regression
Antônio H. Ribeiro, Dave Zachariah, Thomas B. Schön
State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to de…
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…
Robust Learning in Heterogeneous Contexts
Muhammad Osama, Dave Zachariah, Petre Stoica
We consider the problem of learning from training data obtained in different contexts, where the underlying context distribution is unknown and is estimated empirically. We develop…
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…
Calibration tests in multi-class classification: A unifying framework
David Widmann, Fredrik Lindsten, Dave Zachariah
In safety-critical applications a probabilistic model is usually required to be calibrated, i.e., to capture the uncertainty of its predictions accurately. In multi-class classific…
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…
Composite Gaussian Processes: Scalable Computation and Performance Analysis
Xiuming Liu, Dave Zachariah, Edith C. H. Ngai
Gaussian process (GP) models provide a powerful tool for prediction but are computationally prohibitive using large data sets. In such scenarios, one has to resort to approximate m…
How consistent is my model with the data? Information-Theoretic Model Check
Andreas Svensson, Dave Zachariah, Thomas B. Schön
The choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-bo…
Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes
Arun Venkitaraman, Dave Zachariah
We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-fr…
Recursive nonlinear-system identification using latent variables
Per Mattsson, Dave Zachariah, Petre Stoica
In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a lat…
Prediction performance after learning in Gaussian process regression
Johan Wågberg, Dave Zachariah, Thomas B. Schön +1
This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical…