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Found 30 papers, 18 papers with code
Adaptive Parameter-Free Robust Learning using Latent Bernoulli Variables
We present an efficient parameter-free approach for statistical learning from corrupted training sets.
Externally Valid Policy Evaluation Combining Trial and Observational Data
Trial data is, however, drawn from a population which may differ from the intended target population and this raises a problem of external validity (aka.
Regularization properties of adversarially-trained linear regression
And, conversely, the minimum-norm interpolator is the solution to adversarial training with a given radius.
Off-Policy Evaluation with Out-of-Sample Guarantees
We consider the problem of evaluating the performance of a decision policy using past observational data.
Calibration tests beyond classification
In the machine learning literature, different measures and statistical tests have been proposed and studied for evaluating the calibration of classification models.
Diagnostic Tool for Out-of-Sample Model Evaluation
Assessment of model fitness is a key part of machine learning.
Surprises in adversarially-trained linear regression
We prove that adversarial training with small disturbances gives the solution with the minimum-norm that interpolates the training data.
Tuned Regularized Estimators for Linear Regression via Covariance Fitting
We start by showing that three known optimal linear estimators belong to a wider class of estimators that can be formulated as a solution to a weighted and constrained minimization problem.
Learning Pareto-Efficient Decisions with Confidence
The paper considers the problem of multi-objective decision support when outcomes are uncertain.
Robust Learning in Heterogeneous Contexts
We consider the problem of learning from training data obtained in different contexts, where the underlying context distribution is unknown and is estimated empirically.
Inference of Causal Effects when Control Variables are Unknown
We verify the capability of the method to produce valid confidence intervals for average causal effects using synthetic data, even when the appropriate specification of control variables is unknown.
Robust Localization in Wireless Networks From Corrupted Signals
We address the problem of timing-based localization in wireless networks, when an unknown fraction of data is corrupted by nonideal signal conditions.
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
A spatial point process can be characterized by an intensity function which predicts the number of events that occur across space.
Learning Robust Decision Policies from Observational Data
We address the problem of learning a decision policy from observational data of past decisions in contexts with features and associated outcomes.
A latent variable approach to heat load prediction in thermal grids
In this paper a new method for heat load prediction in district energy systems is proposed.
Robust Prediction when Features are Missing
The robustness properties of the approach are demonstrated on both real and synthetic data.
Calibration tests in multi-class classification: A unifying framework
In safety-critical applications a probabilistic model is usually required to be calibrated, i. e., to capture the uncertainty of its predictions accurately.
Robust Risk Minimization for Statistical Learning
We consider a general statistical learning problem where an unknown fraction of the training data is corrupted.
Effect Inference from Two-Group Data with Sampling Bias
In many applications, different populations are compared using data that are sampled in a biased manner.
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
We address the problem of inferring the causal effect of an exposure on an outcome across space, using observational data.
Reliable Semi-Supervised Learning when Labels are Missing at Random
Semi-supervised learning methods are motivated by the availability of large datasets with unlabeled features in addition to labeled data.
Data Consistency Approach to Model Validation
The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data.
Learning Localized Spatio-Temporal Models From Streaming Data
We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream.
Composite Gaussian Processes: Scalable Computation and Performance Analysis
Gaussian process (GP) models provide a powerful tool for prediction but are computationally prohibitive using large data sets.
Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes
We address the problem of prediction of multivariate data process using an underlying graph model.
How consistent is my model with the data? Information-Theoretic Model Check
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-box.
Model-Robust Counterfactual Prediction Method
We develop a novel method for counterfactual analysis based on observational data using prediction intervals for units under different exposures.
Online Learning for Distribution-Free Prediction
We develop an online learning method for prediction, which is important in problems with large and/or streaming data sets.
Recursive nonlinear-system identification using latent variables
In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs.
Prediction performance after learning in Gaussian process regression
Starting from a generalization of the Cram\'er-Rao bound, we derive a more accurate MSE bound which provides a measure of uncertainty for prediction of Gaussian processes.
