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Supervisory teleoperation with online learning and optimal control

Publications associées (37)

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Discovery and Temporal Analysis of MOOC Study Patterns

Pierre Dillenbourg, Mina Shirvani Boroujeni

The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals' learning processes and could facilitate the design of personaliz ...

2019

Small-variance asymptotics for non-parametric online robot learning

Sylvain Calinon, Ajay Kumar Tanwani

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-va ...

SAGE PUBLICATIONS LTD2019

Cover: A Cluster-Based Variance Reduced Method For Online Learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

In this paper, we develop a stochastic-gradient learning algorithm for situations involving streaming data that arise from an underlying clustered structure. In such settings, the variance of gradient noise can be decomposed into the in-cluster variance si ...

IEEE2019

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Ali H. Sayed, Jie Chen

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In ...

2017

Traffic Pattern Analysis and Anomaly Detection via Probabilistic Inference Model

Kwang Moo Yi

In this chapter, we introduce a method for trajectory pattern analysis through the probabilistic inference model with both regional and velocity observations. By embedding Gaussian models into the discrete topic model framework, our method uses continuous ...

Springer Netherlands2016

The brain strategy for online learning

Ali H. Sayed, Stefan Vlaski, Bicheng Ying

Complexity is a double-edged sword for learning algorithms when the number of available samples for training in relation to the dimension of the feature space is small. This is because simple models do not sufficiently capture the nuances of the data set, ...

IEEE2016

Big-Data Streaming Applications Scheduling Based on Staged Multi-armed Bandits

David Atienza Alonso, Karim Kanoun, Cem Tekin

Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and ...

Institute of Electrical and Electronics Engineers2016

Big-Data Streaming Applications Scheduling Based on Staged Multi-Armed Bandits

David Atienza Alonso, Karim Kanoun, Cem Tekin

Institute of Electrical and Electronics Engineers2016

Learning to Track: Online Multi-object Tracking by Decision Making

Alexandre Massoud Alahi

Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation and autonomous driving. In tracking-by-detection, a major challenge of online MOT is how to robustly associate noisy object detecti ...

IEEE2015

The Smart Wind Turbine

Denis Gillet, Christophe Salzmann, Wissam Halimi

Remote experimentation is at the core of Science Technology Engineering and Mathematics education supported by e-learning. The development and integration of remote labo- ratories in online learning activities is hindered by the inherited supporting infras ...

2015