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Found 20 papers, 0 papers with code
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations
Can the parameters of a hidden Markov model (HMM) be estimated from a single sweep through the observations -- and additionally, without being trapped at a local optimum in the likelihood surface?
A Biologically-Inspired Computational Model of Time Perception
We conclude that the agent is able to perceive time similarly to animals when it comes to their intrinsic mechanisms of interpreting time and performing time-aware actions.
Optimal Transport for Correctional Learning
In this framework, an expert agent, referred to as the teacher, modifies the data used by a learning agent, known as the student, to improve its estimation process.
Prediction-Based Leader-Follower Rendezvous Model Predictive Control with Robustness to Communication Losses
In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV).
Robust Trajectory Tracking for Underactuated Quadrotors with Prescribed Performance
We propose a control protocol based on the prescribed performance control (PPC) methodology for a quadrotor unmanned aerial vehicle (UAV).
A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning
A classical learning setting typically concerns an agent/student who collects data, or observations, from a system in order to estimate a certain property of interest.
Aperiodic Communication for MPC in Autonomous Cooperative Landing
The main contribution is a rendezvous algorithm with an online update rule of the rendezvous location.
Learning Models of Model Predictive Controllers using Gradient Data
As a proof of concept, we apply this approach to explicit MPC (eMPC), for which the feedback law is a piece-wise affine function of the state, but the number of pieces grows rapidly with the state dimension.
Cooperative System Identification via Correctional Learning
We consider a cooperative system identification scenario in which an expert agent (teacher) knows a correct, or at least a good, model of the system and aims to assist a learner-agent (student), but cannot directly transfer its knowledge to the student.
Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm
The step-length policy is learned from data of similar optimization problems, avoids additional evaluations of the objective function, and guarantees that the output step remains inside a pre-defined interval.
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression
Our hypothesis is that the use of tasksimilarity helps meta-learning when the available tasks are limited and may contain outlier/ dissimilar tasks.
On Training and Evaluation of Neural Network Approaches for Model Predictive Control
The contribution of this paper is a framework for training and evaluation of Model Predictive Control (MPC) implemented using constrained neural networks.
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)
In this paper, we study how to replicate neural mechanisms involved in time perception, allowing robots to take a step towards temporal cognition.
Learning sparse linear dynamic networks in a hyper-parameter free setting
We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting.
Recursive Prediction of Graph Signals with Incoming Nodes
Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph.
Inverse Filtering for Hidden Markov Models
This paper considers a number of related inverse filtering problems for hidden Markov models (HMMs).
Evaluation of Spectral Learning for the Identification of Hidden Markov Models
Often, when applied in practice, the parameters of these models have to be estimated.
How to monitor and mitigate stair-casing in l1 trend filtering
It is known that TV denoising suffers from the so-called stair-case effect, which leads to detecting false change points.
Approximate Regularization Path for Nuclear Norm Based H2 Model Reduction
This paper concerns model reduction of dynamical systems using the nuclear norm of the Hankel matrix to make a trade-off between model fit and model complexity.
On change point detection using the fused lasso method
In this paper we analyze the asymptotic properties of l1 penalized maximum likelihood estimation of signals with piece-wise constant mean values and/or variances.
