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Found 22 papers, 9 papers with code
Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA
We also propose two important reformulations of the fair PCA problem: a) fair robust PCA -- which can handle outliers in the data, and b) fair sparse PCA -- which can enforce sparsity on the estimated fair principal components.
Pearson-Matthews correlation coefficients for binary and multinary classification and hypothesis testing
We also present an additional new metric for multinary classification which can be viewed as a direct extension of MCC.
Off-Policy Evaluation with Out-of-Sample Guarantees
We consider the problem of evaluating the performance of a decision policy using past observational data.
The Cramer-Rao Bound for Signal Parameter Estimation from Quantized Data
Moreover, the CRB is an achievable limit, for instance it is asymptotically attained by the maximum likelihood estimator (under regularity conditions), and thus it is a useful benchmark to which the accuracy of any parameter estimator can and should be compared.
Diagnostic Tool for Out-of-Sample Model Evaluation
Assessment of model fitness is a key part of machine learning.
Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals
The proposed algorithm does not require tuning of any hyperparameter and it has the desirable feature of eliminating the inactive variables in the course of the iterations - which can help speeding up the algorithm.
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.
Weighted SPICE Algorithms for Range-Doppler Imaging Using One-Bit Automotive Radar
The recently proposed hyperparameter-free (and hence user friendly) weighted SPICE algorithms, including SPICE, LIKES, SLIM and IAA, achieve excellent parameter estimation performance for data sampled with high precision.
Sinusoidal Parameter Estimation from Signed Measurements via Majorization-Minimization Based RELAX
In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function.
Joint RFI Mitigation and Radar Echo Recovery for One-Bit UWB Radar
Radio frequency interference (RFI) mitigation and radar echo recovery are critically important for the proper functioning of ultra-wideband (UWB) radar systems using one-bit sampling techniques.
RFI Mitigation for One-bit UWB Radar Systems
A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the Continuous Time Binary Value (CTBV) technology.
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.
Robust Prediction when Features are Missing
The robustness properties of the approach are demonstrated on both real and synthetic data.
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.
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.
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.
