Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Gaudio, Joseph E."'
Autor:
Gibson, Travis E., Acharya, Sawal, Parashar, Anjali, Gaudio, Joseph E., Annaswamy, Anurdha M.
Gradient based optimization algorithms deployed in Machine Learning (ML) applications are often analyzed and compared by their convergence rates or regret bounds. While these rates and bounds convey valuable information they don't always directly tra
Externí odkaz:
http://arxiv.org/abs/2405.13765
Autor:
Annaswamy, Anuradha M., Guha, Anubhav, Cui, Yingnan, Tang, Sunbochen, Fisher, Peter A., Gaudio, Joseph E.
This paper considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. We propose a combination of a Reinforcement Learning (RL) based policy in the outer loop suitably chosen to ensure stability
Externí odkaz:
http://arxiv.org/abs/2105.06577
We propose two algorithms for discrete-time parameter estimation, one for time-varying parameters under persistent excitation (PE) condition, another for constant parameters under no PE condition. For the first algorithm, we show that in the presence
Externí odkaz:
http://arxiv.org/abs/2103.16653
Gradient-descent based iterative algorithms pervade a variety of problems in estimation, prediction, learning, control, and optimization. Recently iterative algorithms based on higher-order information have been explored in an attempt to lead to acce
Externí odkaz:
http://arxiv.org/abs/2103.12868
Analysis and synthesis of safety-critical autonomous systems are carried out using models which are often dynamic. Two central features of these dynamic systems are parameters and unmodeled dynamics. This paper addresses the use of a spectral lines-b
Externí odkaz:
http://arxiv.org/abs/2006.12687
Autor:
Gaudio, Joseph E., Annaswamy, Anuradha M., Moreu, José M., Bolender, Michael A., Gibson, Travis E.
High order momentum-based parameter update algorithms have seen widespread applications in training machine learning models. Recently, connections with variational approaches have led to the derivation of new learning algorithms with accelerated lear
Externí odkaz:
http://arxiv.org/abs/2005.01529
This paper presents a new parameter estimation algorithm for the adaptive control of a class of time-varying plants. The main feature of this algorithm is a matrix of time-varying learning rates, which enables parameter estimation error trajectories
Externí odkaz:
http://arxiv.org/abs/1911.03810
Input constraints as well as parametric uncertainties must be accounted for in the design of safe control systems. This paper presents an adaptive controller for multiple-input-multiple-output (MIMO) plants with input magnitude and rate saturation in
Externí odkaz:
http://arxiv.org/abs/1907.11913
Autor:
Gaudio, Joseph E., Gibson, Travis E., Annaswamy, Anuradha M., Bolender, Michael A., Lavretsky, Eugene
This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning. Starting from common output error formulations, similarities in update law modifications are examined. Concept
Externí odkaz:
http://arxiv.org/abs/1904.05856
Features in machine learning problems are often time-varying and may be related to outputs in an algebraic or dynamical manner. The dynamic nature of these machine learning problems renders current higher order accelerated gradient descent methods un
Externí odkaz:
http://arxiv.org/abs/1903.04666