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01/20/2023
Offline Policy Evaluation with Out-of-Sample Guarantees
We consider the problem of evaluating the performance of a decision poli...
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10/21/2022
Calibration tests beyond classification
Most supervised machine learning tasks are subject to irreducible predic...
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06/22/2022
Diagnostic Tool for Out-of-Sample Model Evaluation
Assessment of model fitness is an important step in many problems. Model...
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05/25/2022
Surprises in adversarially-trained linear regression
State-of-the-art machine learning models can be vulnerable to very small...
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01/21/2022
Tuned Regularized Estimators for Linear Regression via Covariance Fitting
We consider the problem of finding tuned regularized parameter estimator...
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10/19/2021
Learning Pareto-Efficient Decisions with Confidence
The paper considers the problem of multi-objective decision support when...
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05/18/2021
Distributionally Robust Learning in Heterogeneous Contexts
We consider the problem of learning from training data obtained in diffe...
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12/15/2020
Inference of Causal Effects when Adjustment Sets are Unknown
Conventional methods in causal effect inference typically rely on specif...
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10/09/2020
Robust Localization in Wireless Networks From Corrupted Signals
We address the problem of timing-based localization in wireless networks...
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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...
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06/03/2020
Learning Robust Decision Policies from Observational Data
We address the problem of learning a decision policy from observational ...
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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...
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12/16/2019
Robust Prediction when Features are Missing
Predictors are learned using past training data containing features whic...
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10/24/2019
Calibration tests in multi-class classification: A unifying framework
In safety-critical applications a probabilistic model is usually require...
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10/03/2019
Robust Risk Minimization for Statistical Learning
We consider a general statistical learning problem where an unknown frac...
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02/26/2019
Effect Inference from Two-Group Data with Sampling Bias
In many applications, different populations are compared using data that...
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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 ...
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11/27/2018
Robust Semi-Supervised Learning when Labels are Missing at Random
Semi-supervised learning methods are motivated by the relative paucity o...
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08/17/2018
Data Consistency Approach to Model Validation
In scientific inference problems, the underlying statistical modeling as...
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02/09/2018
Learning Localized Spatio-Temporal Models From Streaming Data
We address the problem of predicting spatio-temporal processes with temp...
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01/31/2018
Composite Gaussian Processes: Scalable Computation and Performance Analysis
Gaussian process (GP) models provide a powerful tool for prediction but ...
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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 ...
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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...
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12/07/2017
Is My Model Flexible Enough? Information-Theoretic Model Check
The choice of model class is fundamental in statistical learning and sys...
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05/19/2017
Model-Robust Counterfactual Prediction Method
We develop a method for assessing counterfactual predictions with multip...
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03/15/2017
Online Learning for Distribution-Free Prediction
We develop an online learning method for prediction, which is important ...
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06/14/2016
Recursive nonlinear-system identification using latent variables
In this paper we develop a method for learning nonlinear systems with mu...
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06/13/2016