Papers
546 papers found
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines
Kun Wang, Mridul Aanjaneya, Kostas Bekris
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
Jia-Jie Zhu, Bernhard Schoelkopf, Moritz Diehl
A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent
Jasmine Sekhon, Cody Fleming
A Theoretical Analysis of Deep Q-Learning
Jianqing Fan, Zhaoran Wang, Yuchen Xie et al.
Bayesian joint state and parameter tracking in autoregressive models
Ismail Senoz, Albert Podusenko, Wouter M. Kouw et al.
Bayesian Learning with Adaptive Load Allocation Strategies
Manxi Wu, Saurabh Amin, Asuman Ozdaglar
Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling
Kim Peter Wabersich, Melanie Zeilinger
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach
Mirko Mazzoleni, Matteo Scandella, Simone Formentin et al.
Constrained Upper Confidence Reinforcement Learning
Liyuan Zheng, Lillian Ratliff
Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors
Aidan Laracy, Hamid Ossareh
Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability
Max Revay, Ian Manchester
Counterfactual Programming for Optimal Control
Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Data-Driven Distributed Predictive Control via Network Optimization
Ahmed Allibhoy, Jorge Cortes
Data-driven distributionally robust LQR with multiplicative noise
Peter Coppens, Mathijs Schuurmans, Panagiotis Patrinos
Data-driven Identification of Approximate Passive Linear Models for Nonlinear Systems
S. Sivaranjani, Etika Agarwal, Vijay Gupta
Direct Data-Driven Control with Embedded Anti-Windup Compensation
Valentina Breschi, Simone Formentin
Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach
Yingying Li, Yujie Tang, Runyu Zhang et al.
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty
Elena Arcari, Lukas Hewing, Max Schlichting et al.
Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions
Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal et al.
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
Giovanni Sutanto, Austin Wang, Yixin Lin et al.
Estimating Reachable Sets with Scenario Optimization
Alex Devonport, Murat Arcak
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Muhammad Asif Rana, Anqi Li, Dieter Fox et al.
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint
Monimoy Bujarbaruah, Charlott Vallon
Faster saddle-point optimization for solving large-scale Markov decision processes
Joan Bas Serrano, Gergely Neu
Feed-forward Neural Networks with Trainable Delay
Xunbi A. Ji, Tamás G. Molnár, Sergei S. Avedisov et al.