60 citations · 250 across the 32 of their papers we have counts for
15 papers · 1 filter
Structural Risk Minimization for Learning Nonlinear Dynamics
Charis Stamouli, Evangelos Chatzipantazis, George J. Pappas
Recent advances in learning or identification of nonlinear dynamics focus on learning a suitable model within a pre-specified model class. However, a key difficulty that remains is…
Navigation with shadow prices to optimize multi-commodity flow rates
Ignacio Boero, Igor Spasojevic, Mariana del Castillo +3
We propose a method for providing communication network infrastructure in autonomous multi-agent teams. In particular, we consider a set of communication agents that are placed alo…
Safety Filter Design for Neural Network Systems via Convex Optimization
Shaoru Chen, Kong Yao Chee, Nikolai Matni +2
With the increase in data availability, it has been widely demonstrated that neural networks (NN) can capture complex system dynamics precisely in a data-driven manner. However, th…
Enhancing Sample Efficiency and Uncertainty Compensation in Learning-based Model Predictive Control for Aerial Robots
Kong Yao Chee, Thales C. Silva, M. Ani Hsieh +1
The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despit…
Adversarial Training Should Be Cast as a Non-Zero-Sum Game
Alexander Robey, Fabian Latorre, George J. Pappas +2
One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are t…
Active Collaborative Localization in Heterogeneous Robot Teams
Igor Spasojevic, Xu Liu, Alejandro Ribeiro +2
Accurate and robust state estimation is critical for autonomous navigation of robot teams. This task is especially challenging for large groups of size, weight, and power (SWAP) co…
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification
Thomas Beckers, Tom Z. Jiahao, George J. Pappas
Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes. The dy…
Physics-enhanced Gaussian Process Variational Autoencoder
Thomas Beckers, Qirui Wu, George J. Pappas
Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data. Using video clips as input data, the encoder may be used to de…
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling
Nicolò Dal Fabbro, Aritra Mitra, George J. Pappas
Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints. However, w…
Resilient Temporal Logic Planning in the Presence of Robot Failures
Samarth Kalluraya, George J. Pappas, Yiannis Kantaros
Several task and motion planning algorithms have been proposed recently to design paths for mobile robot teams with collaborative high-level missions specified using formal languag…
Conformal Prediction Regions for Time Series using Linear Complementarity Programming
Matthew Cleaveland, Insup Lee, George J. Pappas +1
Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction…
Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction
Shuo Yang, George J. Pappas, Rahul Mangharam +1
We consider perception-based control using state estimates that are obtained from high-dimensional sensor measurements via learning-enabled perception maps. However, these percepti…
Variational Autoencoding Neural Operators
Jacob H. Seidman, Georgios Kissas, George J. Pappas +1
Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate modeling and physical systems. A natur…
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
Han Wang, Aritra Mitra, Hamed Hassani +2
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves agents interacting wi…
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning
Aritra Mitra, George J. Pappas, Hamed Hassani
In large-scale distributed machine learning, recent works have studied the effects of compressing gradients in stochastic optimization to alleviate the communication bottleneck. Th…