Brian M. Sadler

research

∙ 08/30/2023

Octonion Phase Retrieval

Signal processing over hypercomplex numbers arises in many optical imagi...

0 Roman Jacome, et al. ∙

research

∙ 06/09/2023

Ada-NAV: Adaptive Trajectory-Based Sample Efficient Policy Learning for Robotic Navigation

Reinforcement learning methods, while effective for learning robotic nav...

0 Bhrij Patel, et al. ∙

research

∙ 03/23/2023

Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization

Joint radar-communications (JRC) has emerged as a promising technology f...

0 Roman Jacome, et al. ∙

research

∙ 01/28/2023

Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic

Many existing reinforcement learning (RL) methods employ stochastic grad...

0 Wesley A. Suttle, et al. ∙

research

∙ 12/08/2022

LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

This study focuses on embodied agents that can follow natural language i...

0 Chan Hee Song, et al. ∙

research

∙ 11/16/2022

Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications

Recent interest in integrated sensing and communications has led to the ...

0 Jonathan Monsalve, et al. ∙

research

∙ 11/13/2022

Identifying Coordination in a Cognitive Radar Network – A Multi-Objective Inverse Reinforcement Learning Approach

Consider a target being tracked by a cognitive radar network. If the tar...

0 Luke Snow, et al. ∙

research

∙ 09/24/2022

Communication-Efficient Federated Learning Using Censored Heavy Ball Descent

Distributed machine learning enables scalability and computational offlo...

0 Yicheng Chen, et al. ∙

research

∙ 08/18/2022

Quickest Detection for Human-Sensor Systems using Quantum Decision Theory

In mathematical psychology, recent models for human decision-making use ...

0 Luke Snow, et al. ∙

research

∙ 08/08/2022

Dual-Blind Deconvolution for Overlaid Radar-Communications Systems

The increasingly crowded spectrum has spurred the design of joint radar-...

0 Edwin Vargas, et al. ∙

research

∙ 06/22/2022

: Calibrating Global and Local Models via Federated Learning Beyond Consensus

In federated learning (FL), the objective of collaboratively learning a ...

2 Amrit Singh Bedi, et al. ∙

research

∙ 06/10/2022

Multi-dimensional dual-blind deconvolution approach toward joint radar-communications

We consider a joint multiple-antenna radar-communications system in a co...

0 Roman Jacome, et al. ∙

research

∙ 06/02/2022

Deceptive Planning for Resource Allocation

We consider a team of autonomous agents that navigate in an adversarial ...

0 Yagiz Savas, et al. ∙

research

∙ 06/02/2022

Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning

In this work, we propose a novel Kernelized Stein Discrepancy-based Post...

8 Souradip Chakraborty, et al. ∙

research

∙ 02/14/2022

One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones

We study the problem of developing autonomous agents that can follow hum...

0 Chan Hee Song, et al. ∙

research

∙ 02/05/2022

Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting

The challenge of communication-efficient distributed optimization has at...

0 Yicheng Chen, et al. ∙

research

∙ 02/05/2022

Distributed Learning With Sparsified Gradient Differences

A very large number of communications are typically required to solve di...

0 Yicheng Chen, et al. ∙

research

∙ 01/27/2022

Phase Retrieval for Radar Waveform Design

The ability of a radar to discriminate in both range and Doppler velocit...

0 Samuel Pinilla, et al. ∙

research

∙ 11/26/2021

Non-Convex Recovery from Phaseless Low-Resolution Blind Deconvolution Measurements using Noisy Masked Patterns

This paper addresses recovery of a kernel h∈ℂ^n and a signal x∈ℂ^n from ...

0 Samuel Pinilla, et al. ∙

research

∙ 11/11/2021

Unique Bispectrum Inversion for Signals with Finite Spectral/Temporal Support

Retrieving a signal from the Fourier transform of its third-order statis...

0 Samuel Pinilla, et al. ∙

research

∙ 11/11/2021

Joint Radar-Communications Processing from a Dual-Blind Deconvolution Perspective

We consider a general spectral coexistence scenario, wherein the channel...

0 Edwin Vargas, et al. ∙

research

∙ 09/25/2021

Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems

Robustness is key to engineering, automation, and science as a whole. Ho...

4 Amanda Prorok, et al. ∙

research

∙ 06/24/2021

Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

In this paper, we present a perception-action-communication loop design ...

0 Ting-Kuei Hu, et al. ∙

research

∙ 06/05/2021

Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping

Graph neural networks (GNNs) are processing architectures that exploit g...

0 Zhan Gao, et al. ∙

research

∙ 12/17/2020

Secrecy of Multi-Antenna Transmission with Full-Duplex User in the Presence of Randomly Located Eavesdroppers

This paper considers the secrecy performance of several schemes for mult...

0 Ishmam Zabir, et al. ∙

research

∙ 03/27/2020

Distributed Beamforming for Agents with Localization Errors

We consider a scenario in which a group of agents aim to collectively tr...

0 Erfaun Noorani, et al. ∙

research

∙ 02/06/2020

VGAI: A Vision-Based Decentralized Controller Learning Framework for Robot Swarms

Despite the popularity of decentralized controller learning, very few su...

0 Ting-Kuei Hu, et al. ∙

research

∙ 10/17/2019

HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion

Taxonomies are of great value to many knowledge-rich applications. As th...

25 Jiaming Shen, et al. ∙

research

∙ 09/25/2019

Optimally Compressed Nonparametric Online Learning

Batch training of machine learning models based on neural networks is no...

0 Alec Koppel, et al. ∙

research

∙ 09/23/2019

Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates

In Bayesian inference, we seek to compute information about random varia...

0 Alec Koppel, et al. ∙

research

∙ 09/12/2019

Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony

An open challenge in supervised learning is conceptual drift: a data poi...

0 Amrit Singh Bedi, et al. ∙

research

∙ 08/17/2018

Decentralized Dictionary Learning Over Time-Varying Digraphs

This paper studies Dictionary Learning problems wherein the learning tas...

0 Amir Daneshmand, et al. ∙

Brian M. Sadler

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