Dave Zachariah | DeepAI

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Featured Co-authors

Thomas B. Schön
81 publications
Fredrik Lindsten
37 publications
Petre Stoica
21 publications
Antônio H. Ribeiro
15 publications
Muhammad Osama
14 publications
Arun Venkitaraman
14 publications
Andreas Svensson
11 publications
Satyam Dwivedi
9 publications
Thomas Schön
9 publications
Edith C. H. Ngai
7 publications
David Widmann
4 publications

∙ 01/20/2023

Offline Policy Evaluation with Out-of-Sample Guarantees

We consider the problem of evaluating the performance of a decision poli...

0 Sofia Ek, et al. ∙

∙ 10/21/2022

Calibration tests beyond classification

Most supervised machine learning tasks are subject to irreducible predic...

0 David Widmann, et al. ∙

∙ 06/22/2022

Diagnostic Tool for Out-of-Sample Model Evaluation

Assessment of model fitness is an important step in many problems. Model...

0 Ludvig Hult, et al. ∙

∙ 05/25/2022

Surprises in adversarially-trained linear regression

State-of-the-art machine learning models can be vulnerable to very small...

0 Antônio H. Ribeiro, et al. ∙

∙ 01/21/2022

Tuned Regularized Estimators for Linear Regression via Covariance Fitting

We consider the problem of finding tuned regularized parameter estimator...

0 Per Mattsson, et al. ∙

∙ 10/19/2021

Learning Pareto-Efficient Decisions with Confidence

The paper considers the problem of multi-objective decision support when...

0 Sofia Ek, et al. ∙

∙ 05/18/2021

Distributionally Robust Learning in Heterogeneous Contexts

We consider the problem of learning from training data obtained in diffe...

0 Muhammad Osama, et al. ∙

∙ 12/15/2020

Inference of Causal Effects when Adjustment Sets are Unknown

Conventional methods in causal effect inference typically rely on specif...

0 Ludvig Hult, et al. ∙

∙ 10/09/2020

Robust Localization in Wireless Networks From Corrupted Signals

We address the problem of timing-based localization in wireless networks...

0 Muhammad Osama, et al. ∙

∙ 07/03/2020

Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees

A spatial point process can be characterized by an intensity function wh...

0 Muhammad Osama, et al. ∙

∙ 06/03/2020

Learning Robust Decision Policies from Observational Data

We address the problem of learning a decision policy from observational ...

0 Muhammad Osama, et al. ∙

∙ 02/13/2020

A latent variable approach to heat load prediction in thermal grids

In this paper a new method for heat load prediction in district energy s...

0 Johan Simonsson, et al. ∙

∙ 12/16/2019

Robust Prediction when Features are Missing

Predictors are learned using past training data containing features whic...

0 Xiuming Liu, et al. ∙

∙ 10/24/2019

Calibration tests in multi-class classification: A unifying framework

In safety-critical applications a probabilistic model is usually require...

14 David Widmann, et al. ∙

∙ 10/03/2019

Robust Risk Minimization for Statistical Learning

We consider a general statistical learning problem where an unknown frac...

0 Muhammad Osama, et al. ∙

∙ 02/26/2019

Effect Inference from Two-Group Data with Sampling Bias

In many applications, different populations are compared using data that...

0 Dave Zachariah, et al. ∙

∙ 01/28/2019

Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding

We address the problem of inferring the causal effect of an exposure on ...

0 Muhammad Osama, et al. ∙

∙ 11/27/2018

Robust Semi-Supervised Learning when Labels are Missing at Random

Semi-supervised learning methods are motivated by the relative paucity o...

0 Xiuming Liu, et al. ∙

∙ 08/17/2018

Data Consistency Approach to Model Validation

In scientific inference problems, the underlying statistical modeling as...

0 Andreas Svensson, et al. ∙

∙ 02/09/2018

Learning Localized Spatio-Temporal Models From Streaming Data

We address the problem of predicting spatio-temporal processes with temp...

0 Muhammad Osama, et al. ∙

∙ 01/31/2018

Composite Gaussian Processes: Scalable Computation and Performance Analysis

Gaussian process (GP) models provide a powerful tool for prediction but ...

0 Xiuming Liu, et al. ∙

∙ 12/12/2017

Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes

We address the problem of prediction and filtering of multivariate data ...

0 Arun Venkitaraman, et al. ∙

∙ 12/07/2017

How consistent is my model with the data? Information-Theoretic Model Check

The choice of model class is fundamental in statistical learning and sys...

0 Andreas Svensson, et al. ∙

∙ 12/07/2017

Is My Model Flexible Enough? Information-Theoretic Model Check

The choice of model class is fundamental in statistical learning and sys...

0 Andreas Svensson, et al. ∙

∙ 05/19/2017

Model-Robust Counterfactual Prediction Method

We develop a method for assessing counterfactual predictions with multip...

0 Dave Zachariah, et al. ∙

∙ 03/15/2017

Online Learning for Distribution-Free Prediction

We develop an online learning method for prediction, which is important ...

0 Dave Zachariah, et al. ∙

∙ 06/14/2016

Recursive nonlinear-system identification using latent variables

In this paper we develop a method for learning nonlinear systems with mu...

0 Per Mattsson, et al. ∙

∙ 06/13/2016

Prediction performance after learning in Gaussian process regression

This paper considers the quantification of the prediction performance in...

0 Johan Wågberg, et al. ∙

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