IEEE World Congress on Computational Intelligence (WCCI 2020)
International Joint Conference on Neural Networks (IJCNN 2020)

IJCNN 2020 Special Session 

on Secure Learning 

Call for Papers

Selected papers will be invited to Complex & Intelligent Systems Journal Special Issue

Glasgow, Scotland, UK: July 19-24, 2020 



IEEE World Congress on Computational Intelligence (WCCI 2020)
International Joint Conference on Neural Networks (IJCNN 2020)

IJCNN 2020 Special Session on Secure Learning 

Call for papers

Selected papers will be invited to Complex & Intelligent Systems Journal Special Issue 


There has been growing interest in rectifying machine learning vulnerabilities and preserving privacy. Adversarial machine learning and privacy-preserving has attracted tremendous attention in the machine learning society over the past few years.  Recent research has studied the vulnerability of machine learning algorithms and various defense mechanisms against those vulnerabilities. The questions surrounding this space are more pressing and relevant than ever before: How can we make a system robust to novel or potentially adversarial inputs? How can machine learning systems detect and adapt to changes in the environment over time? When can we trust that a system that has performed well in the past will continue to do so in the future? These questions are essential to consider in designing systems for high stakes applications such as self-driving cars and automated surgical assistants.

We aim to bring together researchers in diverse areas such as reinforcement learning, human-robot interaction, game theory, cognitive science, and security to further the field of reliable and trustworthy machine learning. We will focus on robustness, trustworthiness, privacy preservation, and scalability. Robustness refers to the ability to withstand the effects of adversaries, including adversarial examples and poisoning data, distributional shift, model misspecification, and corrupted data. Trustworthiness is guaranteed by transparency, explainability, and privacy preservation. Scalability refers to the ability to generalize to novel situations and objectives.


This workshop aims to promote the most recent advances of secure machine learning from both the theoretical and empirical perspectives as well as novel applications.  The goal is to build reliable machine learning models, which are resilient in adversarial settings.