
ModelBased Robust Deep Learning
While deep learning has resulted in major breakthroughs in many applicat...
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Sample Complexity of Kalman Filtering for Unknown Systems
In this paper, we consider the task of designing a Kalman Filter (KF) fo...
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Learning Qnetwork for Active Information Acquisition
In this paper, we propose a novel Reinforcement Learning approach for so...
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Finite Sample Analysis of Stochastic System Identification
In this paper, we analyze the finite sample complexity of stochastic sys...
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Optimal Algorithms for Submodular Maximization with Distributed Constraints
We consider a class of discrete optimization problems that aim to maximi...
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Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example
The ability to store and manipulate information is a hallmark of computa...
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ModelFree Learning of Optimal Ergodic Policies in Wireless Systems
Learning optimal resource allocation policies in wireless systems can be...
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RiskAware MMSE Estimation
Despite the simplicity and intuitive interpretation of Minimum Mean Squa...
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Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
The fragility of deep neural networks to adversariallychosen inputs has...
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Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Tight estimation of the Lipschitz constant for deep neural networks (DNN...
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Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees
Reinforcement Learning (RL) has emerged as an efficient method of choice...
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Distributed AttackRobust Submodular Maximization for MultiRobot Planning
We aim to guard swarmrobotics applications against denialofservice (D...
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Nonmyopic View Planning for Active Object Detection
One of the central problems in computer vision is the detection of seman...
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Resilient Monotone Submodular Function Maximization
In this paper, we focus on applications in machine learning, optimizatio...
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A MovingHorizon Hybrid Stochastic Game for Secure Control of CyberPhysical Systems
In this paper, we establish a zerosum, hybrid state stochastic game mod...
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Control and Sensing Codesign
LinearQuadraticGaussian (LQG) control is concerned with the design of ...
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Resilient Monotone Sequential Maximization
Applications in machine learning, optimization, and control require the ...
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Dense 3D Mapping with Spatial Correlation via Gaussian Filtering
Constructing an occupancy representation of the environment is a fundame...
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Cloudbased MPC with Encrypted Data
This paper explores the privacy of cloud outsourced Model Predictive Con...
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LQG Control and Sensing Codesign
LinearQuadraticGaussian (LQG) control is concerned with the design of ...
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Resilient NonSubmodular Maximization over Matroid Constraints
Applications in control, robotics, and optimization motivate the design ...
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Resilient Active Information Gathering with Mobile Robots
Applications in robotics, such as multirobot target tracking, involve t...
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On the Limited Communication Analysis and Design for Decentralized Estimation
This paper pertains to the analysis and design of decentralized estimati...
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Resilient Active Target Tracking with Multiple Robots
The problem of target tracking with multiple robots consists of actively...
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Cloudbased Quadratic Optimization with Partially Homomorphic Encryption
The development of largescale distributed control systems has led to th...
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LatencyReliability Tradeoffs for State Estimation
The emerging interest in lowlatency highreliability applications, such...
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Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming
Analyzing the robustness of neural networks against normbounded uncerta...
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Secure Multiparty Computation for Cloudbased Control
In this chapter, we will explore the cloudoutsourced privacypreserving...
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Source Seeking in Unknown Environments with Convex Obstacles
Navigation tasks often cannot be defined in terms of a target, either be...
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Probabilistic Verification and Reachability Analysis of Neural Networks via Semidefinite Programming
Quantifying the robustness of neural networks or verifying their safety ...
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Statistical Learning for Analysis of Networked Control Systems over Unknown Channels
Recent control trends are increasingly relying on communication networks...
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Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller
This paper describes a verification case study on an autonomous racing c...
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Robust and Adaptive Sequential Submodular Optimization
Emerging applications of control, estimation, and machine learning, rang...
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Technical Report: Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback
This paper presents a reactive planning system that enriches the topolog...
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ReachSDP: Reachability Analysis of ClosedLoop Systems with Neural Network Controllers via Semidefinite Programming
There has been an increasing interest in using neural networks in closed...
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George J. Pappas
verfied profile
George J. Pappas is the UPS Foundation Professor and Chair of the Department of Electrical and Systems Engineering at the University of Pennsylvania. He also holds a secondary appointment in the Departments of Computer and Information Sciences, and Mechanical Engineering and Applied Mechanics. He is member of the GRASP Lab and the PRECISE Center.