225 citations · 496 across the 20 of their papers we have counts for
21 papers
On Counterfactual Interventions in Vector Autoregressive Models
Kurt Butler, Marija Iloska, Petar M. Djuric
Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without estab…
Explainable Learning with Gaussian Processes
Kurt Butler, Guanchao Feng, Petar M. Djuric
The field of explainable artificial intelligence (XAI) attempts to develop methods that provide insight into how complicated machine learning methods make predictions. Many methods…
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling
Marzieh Ajirak, Daniel Waxman, Fernando Llorente +1
In science and engineering, we often work with models designed for accurate prediction of variables of interest. Recognizing that these models are approximations of reality, it bec…
Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
Daniel Waxman, Kurt Butler, Petar M. Djuric
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery u…
Gaussian Process-Gated Hierarchical Mixtures of Experts
Yuhao Liu, Marzieh Ajirak, Petar Djuric
In this paper, we propose novel Gaussian process-gated hierarchical mixtures of experts (GPHMEs). Unlike other mixtures of experts with gating models linear in the input, our model…
Sequential Estimation of Gaussian Process-based Deep State-Space Models
Yuhao Liu, Marzieh Ajirak, Petar Djuric
We consider the problem of sequential estimation of the unknowns of state-space and deep state-space models that include estimation of functions and latent processes of the models.…
Networks of UAVs of Low-Complexity for Time-Critical Localization
Anna Guerra, Francesco Guidi, Davide Dardari +1
Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization perf…
Near-field Tracking with Large Antenna Arrays: Fundamental Limits and Practical Algorithms
Anna Guerra, Francesco Guidi, Davide Dardari +1
Applications towards 6G have brought a huge interest towards arrays with a high number of antennas and operating within the millimeter and sub-THz bandwidths for joint communicatio…
Particle Filtering Under General Regime Switching
Yousef El-Laham, Liu Yang, Petar M. Djuric +1
In this paper, we consider a new framework for particle filtering under model uncertainty that operates beyond the scope of Markovian switching systems. Specifically, we develop a…
Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection
Anna Guerra, Francesco Guidi, Davide Dardari +1
In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown e…
Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach
Anna Guerra, Davide Dardari, Petar M. Djuric
Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a lar…
Dynamic Radar Networks of UAVs
Anna Guerra, Davide Dardari, Petar M. Djuric
In the incoming years, the low-aerial space will be crowded by unmanned aerial vehicles (UAVs), which will be providing different services. In this expected context, an emerging pr…
Joint Indoor Localization and Navigation of UAVs for Network Formation Control
Anna Guerra, Davide Dardari, Petar M. Djuric
In this paper, we propose a joint indoor localization and navigation algorithm to enable a swarm of unmanned aerial vehicles (UAVs) to deploy in a specific spatial formation in ind…
Non-Centralized Navigation for Source Localization by Cooperative UAVs
Anna Guerra, Davide Dardari, Petar M. Djuric
In this paper, we propose a distributed solution to the navigation of a population of unmanned aerial vehicles (UAVs) to best localize a static source. The network is considered he…
Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes
Irene Santos, Juan José Murillo-Fuentes, Petar M. Djurić
In this paper, we address the estimation of a time-varying spatial field of received signal strength (RSS) by relying on measurements from randomly placed and not very accurate sen…
Adapting the Number of Particles in Sequential Monte Carlo Methods through an Online Scheme for Convergence Assessment
Víctor Elvira, Joaquín Míguez, Petar M. Djurić
Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the…
A Bayesian Approach to Data Fusion in Sensor Networks
Zhiyuan Weng, Petar Djuric
In this paper, we address the fusion problem in wireless sensor networks, where the cross-correlation between the estimates is unknown. To solve the problem within the Bayesian fra…
A Search-free DOA Estimation Algorithm for Coprime Arrays
Zhiyuan Weng, Petar Djuric
Recently, coprime arrays have been in the focus of research because of their potential in exploiting redundancy in spanning large apertures with fewer elements than suggested by th…
Communications with decode-and-forward relays in mesh networks
Angel Bravo-Santos, Petar M. Djuric
We consider mesh networks composed of groups of relaying nodes which operate in decode-and-forward mode, where each node from a group relays information to all the nodes in the nex…
Likelihood Consensus-Based Distributed Particle Filtering with Distributed Proposal Density Adaptation
Ondrej Hlinka, Franz Hlawatsch, Petar M. Djuric
We present a consensus-based distributed particle filter (PF) for wireless sensor networks. Each sensor runs a local PF to compute a global state estimate that takes into account t…
Likelihood Consensus and Its Application to Distributed Particle Filtering
Ondrej Hlinka, Ondrej Sluciak, Franz Hlawatsch +2
We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task---based on the past and current measure…