Connectionists: [CFP] Human-in-the-loop Machine Learning and Its Applications - SMC2020

[Joni Zhong]

CFP: SMC 2020 Special Session: Human-in-the-loop Machine Learning and Its Applications


Link: https://jonizhong.weebly.com/smc2020.html



Toronto, Canada, OCT 11-14, 2020



Deadline: April 30, 2020



==COVID-19 UPDATE==

We will be closely following the update of COVID-19 and organizing plan of SMC 2020.



Dear all,



We would like to cordially invite you to submit a paper for the Special Session “Human-in-the-loop Machine Learning and Its Applications” in the  IEEE International Conference on Systems, Man, and Cybernetics 2020, held on Oct 11-14, 2020, in Toronto Canada.



Human-in-the-Loop (HIL) means including human feedback into the training loop of the machine learning models in order to facilitate the following requirements:

1) to improve the quality of training and reduce/prevent the error of the model. When the testing error is larger than a certain threshold, the HIL learning model is able to obtain the new data-points from the users in an interactive way.

2) to incorporate the human user labelling to improve the pre-trained models.  During the training of the state-of-the-art models, the quality of the training data-sets is extremely important. One solution to actively incorporate more data is optimizing the models by including the human users’ feedback (e.g. rewards in RL) or new data-points (e.g. supervised learning) to adapt the pre-trained models in different environments.



In the aforementioned requirements, humans are involved in the training process of the algorithms by continuously optimizing the model’s parameters, feeding the data or even adjusting the model itself by meta-learning. From the perspective of algorithm design, a key problem to design a proper training with a human in the loop is how to leverage both active learning from a human and the optimization of the models. In other words, how can we design a proper query strategy depending on different applications and scenarios?



When properly implemented, the HIL is suitable to be applied in real-world applications where the data is sparse. The active learning mechanism built in the model can be helpful which could seek the human’s help in a form of supervised or reinforcement learning.  In this way, proper designs of interactive displays, machines and robots could be of help to obtain the human’s inputs.



Specifically, we are particularly encouraging robotic applications and their experimental deployment using HIL algorithms. We believe that the HIL algorithms will be an effective method to make robotic platforms more adaptive and safer to interact with. The workshop will offer the opportunity for researchers and practitioners in the diverse field where human reinforcement feedback would have a positive impact on the training processes.  The inclusion of HIL would allow robots and machine learning models to use both internal and external feedback to speed up the learning process and also improve its performance.  In many ways, this could allow the models to learn through their own self-reflection as well as the external input from a human.



Topics of interest include, but are not limited to:

Human Guided Reinforcement Learning;

Human-robot Collaboration;

Human-robot Social Interaction;

Dialogue Systems with Human-in-the-loop;

Interpretable Machine Learning with Human-in-the-loop;

Active Learning and Continuous Learning;

Learning by Demonstration;

Human Factors in HCI/HRI, etc.



Important Dates:

-     Deadline for Submission: April 30, 2020

-     Acceptance Notification: May 10, 2020

-     Conference Dates: Oct 11-14, 2020.



Organizers:

Joni Zhong,  Nottingham Trent University, UK

Mark Elshaw, Coventry University, UK

Yanan Li, University of Sussex, UK

Stefan Wermter, University of Hamburg, Germany

Xiaofeng Liu, Hohai University, China



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