Brian M. Sadler
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View article: Optimal Satellite Maneuvers for Spaceborne Jamming Attacks
Optimal Satellite Maneuvers for Spaceborne Jamming Attacks Open
Satellites are becoming exceedingly critical for communication, making them prime targets for cyber-physical attacks. We consider a rogue satellite in low Earth orbit that jams the uplink communication between another satellite and a groun…
View article: Human Immune System Inspired Security for Federated Learning-Empowered Internet of Things
Human Immune System Inspired Security for Federated Learning-Empowered Internet of Things Open
The emergence of the Internet of Things (IoT) has revolutionized service automation, enabling the development of smart applications. However, the vast interconnectivity of IoT devices not only produces large volumes of data but also create…
View article: WaveMax: Radar Waveform Design via Convex Maximization of FrFT Phase Retrieval
WaveMax: Radar Waveform Design via Convex Maximization of FrFT Phase Retrieval Open
The ambiguity function (AF) is a critical tool in radar waveform design, representing the two-dimensional correlation between a transmitted signal and its time-delayed, frequency-shifted version. Obtaining a radar signal to match a specifi…
View article: Direct Preference Optimization for Primitive-Enabled Hierarchical Reinforcement Learning
Direct Preference Optimization for Primitive-Enabled Hierarchical Reinforcement Learning Open
Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods often suffer from two fundamental challenges: (i) non-stationarity, caused by…
View article: Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling
Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling Open
With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion wh…
View article: DIPPER: Direct Preference Optimization to Accelerate Primitive-Enabled Hierarchical Reinforcement Learning
DIPPER: Direct Preference Optimization to Accelerate Primitive-Enabled Hierarchical Reinforcement Learning Open
Learning control policies to perform complex robotics tasks from human preference data presents significant challenges. On the one hand, the complexity of such tasks typically requires learning policies to perform a variety of subtasks, th…
View article: Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives
Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives Open
Causal inference is the idea of cause and effect; this fundamental area of sciences can be applied to problem space associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why,” whereas the effect descri…
View article: PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling Open
In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses th…
View article: Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problem
Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problem Open
This paper introduces a novel non-parametric change detection algorithm to identify eclipse attacks on a blockchain network; the non-parametric algorithm relies only on the empirical mean and variance of the dataset, making it highly adapt…
View article: Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles Open
In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy needs to achieve its stationary distribution, poses a significa…
View article: PerconAI 2024: 3rd Workshop on Pervasive and Resource-Constrained Artificial Intelligence - Welcome and Committees
PerconAI 2024: 3rd Workshop on Pervasive and Resource-Constrained Artificial Intelligence - Welcome and Committees Open
The PeRConAI workshop aims at promoting the circulation of new ideas and research directions on pervasive and resource-constrained artificial intelligence, serving as a forum for practitioners and researchers working on the intersection be…
View article: Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems Open
We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory. R…
View article: On the Vulnerability of LLM/VLM-Controlled Robotics
On the Vulnerability of LLM/VLM-Controlled Robotics Open
In this work, we highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities. While LLM/VLM-controlled robots show impressive performance acros…
View article: Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks
Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks Open
Deceptive path planning (DPP) is the problem of designing a path that hides its true goal from an outside observer. Existing methods for DPP rely on unrealistic assumptions, such as global state observability and perfect model knowledge, a…
View article: Low Frequency Multi-Robot Networking
Low Frequency Multi-Robot Networking Open
Autonomous teams of unmanned ground and air vehicles rely on networking and distributed processing to collaborate as they jointly localize, explore, map, and learn in sometimes difficult and adverse conditions. Co-designed intelligent wire…
View article: Factor Graph Processing for Dual-Blind Deconvolution at ISAC Receiver
Factor Graph Processing for Dual-Blind Deconvolution at ISAC Receiver Open
Integrated sensing and communications (ISAC) systems have gained significant interest because of their ability to jointly and efficiently access, utilize, and manage the scarce electromagnetic spectrum. The co-existence approach toward ISA…
View article: An Invitation to Hypercomplex Phase Retrieval: Theory and Applications
An Invitation to Hypercomplex Phase Retrieval: Theory and Applications Open
Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing intrinsic correlation of the signal dimensions through Clifford algebra. Recently, the hypercomplex representation of the…
View article: Index-Modulated Metasurface Transceiver Design using Reconfigurable Intelligent Surfaces for 6G Wireless Networks
Index-Modulated Metasurface Transceiver Design using Reconfigurable Intelligent Surfaces for 6G Wireless Networks Open
Higher spectral and energy efficiencies are the envisioned defining characteristics of high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which tran…
View article: Octonion Phase Retrieval
Octonion Phase Retrieval Open
Signal processing over hypercomplex numbers arises in many optical imaging applications. In particular, spectral image or color stereo data are often processed using octonion algebra. Recently, the eight-band multispectral image phase reco…
View article: Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning Open
Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and de…
View article: Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation
Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation Open
Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error. However, real-world robotic tasks often suffer from sparse rewards, leading to inefficient exploration and subopt…
View article: Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization
Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization Open
In joint radar-communications (JRC) applications such as secure military receivers, often the radar and communications signals are overlaid in the received signal. In these passive listening outposts, the signals and channels of both radar…
View article: Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic Open
Many existing reinforcement learning (RL) methods employ stochastic gradient iteration on the back end, whose stability hinges upon a hypothesis that the data-generating process mixes exponentially fast with a rate parameter that appears i…
View article: LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models Open
This study focuses on using large language models (LLMs) as a planner for embodied agents that can follow natural language instructions to complete complex tasks in a visually-perceived environment. The high data cost and poor sample effic…
View article: Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications
Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications Open
Recent interest in integrated sensing and communications has led to the design of novel signal processing techniques to recover information from an overlaid radar-communications signal. Here, we focus on a spectral coexistence scenario, wh…
View article: Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach Open
Consider a target being tracked by a cognitive radar network. If the target can intercept some radar network emissions, how can it detect coordination among the radars? By 'coordination' we mean that the radar emissions satisfy Pareto opti…
View article: Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent
Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent Open
Distributed machine learning enables scalability and computational offloading, but requires significant levels of communication. Consequently, communication efficiency in distributed learning settings is an important consideration, especia…
View article: Quickest Detection for Human-Sensor Systems using Quantum Decision Theory
Quickest Detection for Human-Sensor Systems using Quantum Decision Theory Open
In mathematical psychology, recent models for human decision-making use Quantum Decision Theory to capture important human-centric features such as order effects and violation of the sure-thing principle (total probability law). We constru…
View article: Dual-Blind Deconvolution for Overlaid Radar-Communications Systems
Dual-Blind Deconvolution for Overlaid Radar-Communications Systems Open
The increasingly crowded spectrum has spurred the design of joint radar-communications systems that share hardware resources and efficiently use the radio frequency spectrum. We study a general spectral coexistence scenario, wherein the ch…