225 citations · 752 across the 41 of their papers we have counts for
42 papers
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…
Two new algorithms for maximum likelihood estimation of sparse covariance matrices with applications to graphical modeling
Ghania Fatima, Prabhu Babu, Petre Stoica
In this paper, we propose two new algorithms for maximum-likelihood estimation (MLE) of high dimensional sparse covariance matrices. Unlike most of the state of-the-art methods, wh…
Pearson-Matthews correlation coefficients for binary and multinary classification and hypothesis testing
Petre Stoica, Prabhu Babu
The Pearson-Matthews correlation coefficient (usually abbreviated MCC) is considered to be one of the most useful metrics for the performance of a binary classification or hypothes…
Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA
Prabhu Babu, Petre Stoica
In this paper we propose a new iterative algorithm to solve the fair PCA (FPCA) problem. We start with the max-min fair PCA formulation originally proposed in [1] and derive a simp…
Low-rank covariance matrix estimation for factor analysis in anisotropic noise: application to array processing and portfolio selection
Petre Stoica, Prabhu Babu
Factor analysis (FA) or principal component analysis (PCA) models the covariance matrix of the observed data as R = SS' + Σ, where SS' is the low-rank covariance matrix of the fact…
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…
Monte-Carlo Sampling Approach to Model Selection: A Primer
Petre Stoica, Xiaolei Shang, Yuanbo Cheng
Any data modeling exercise has two main components: parameter estimation and model selection. The latter will be the topic of this lecture note. More concretely we will introduce s…
The Cramer-Rao Bound for Signal Parameter Estimation from Quantized Data
Petre Stoica, Xiaolei Shang, Yuanbo Cheng
Several current ultra-wide band applications, such as millimeter wave radar and communication systems, require high sampling rates and therefore expensive and energy-hungry analogt…
MIMO Multifunction RF Systems: Detection Performance and Waveform Design
Bo Tang, Petre Stoica
This paper studies the detection performance of a multiple-input-multiple-output (MIMO) multifunction radio frequency (MFRF) system, which simultaneously supports radar, communicat…
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…
Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals
Ghania Fatima, Aakash Arora, Prabhu Babu +1
In this letter, we propose an algorithm for learning a sparse weighted graph by estimating its adjacency matrix under the assumption that the observed signals vary smoothly over th…
Tuned Regularized Estimators for Linear Regression via Covariance Fitting
Per Mattsson, Dave Zachariah, Petre Stoica
We consider the problem of finding tuned regularized parameter estimators for linear models. We start by showing that three known optimal linear estimators belong to a wider class…
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…
Weighted SPICE Algorithms for Range-Doppler Imaging Using One-Bit Automotive Radar
Xiaolei Shang, Jian Li, Petre Stoica
We consider the problem of range-Doppler imaging using one-bit automotive LFMCW1 or PMCW radar that utilizes one-bit ADC sampling with time-varying thresholds at the receiver. The…
RFI Mitigation for One-bit UWB Radar Systems
Tianyi Zhang, Jiaying Ren, Jian Li +2
Radio frequency interference (RFI) mitigation is critical to the proper operation of ultra-wideband (UWB) radar systems since RFI can severely degrade the radar imaging capability…
Sinusoidal Parameter Estimation from Signed Measurements via Majorization-Minimization Based RELAX
Jiaying Ren, Tianyi Zhang, Jian Li +1
We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with fixed as well as time-varying thresholds. In a previous pape…
Joint RFI Mitigation and Radar Echo Recovery for One-Bit UWB Radar
Tianyi Zhang, Jiaying Ren, Jian Li +2
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 sampli…
Robust Localization in Wireless Networks From Corrupted Signals
Muhammad Osama, Dave Zachariah, Satyam Dwivedi +1
We address the problem of timing-based localization in wireless networks, when an unknown fraction of data is corrupted by nonideal signal conditions. While timing-based techniques…
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…
Learning Robust Decision Policies from Observational Data
Muhammad Osama, Dave Zachariah, Peter Stoica
We address the problem of learning a decision policy from observational data of past decisions in contexts with features and associated outcomes. The past policy maybe unknown and…
Polyphase Waveform Design for MIMO Radar Space Time Adaptive Processing
Bo Tang, Jonathan Tuck, Peter Stoica
We consider the design of polyphase waveforms for ground moving target detection with airborne multiple-input-multiple-output (MIMO) radar. Due to the constant-modulus and finite-a…
Robust Prediction when Features are Missing
Xiuming Liu, Dave Zachariah, Petre Stoica
Predictors are learned using past training data which may contain features that are unavailable at the time of prediction. We develop an approach that is robust against outlying mi…
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…