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New theoretical and algorithmic advances in machine learning with optimal transport
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16th November 2017; 14:00
Speaker: Ievgen Redko, associate professor at Institut National des Sciences Appliquées, Lyon, France.
Abstract: Optimal transportation problem is a powerful technique that has recently found its application in various areas of machine learning including, for instance, computer vision, information retrieval and music unmixing. In this talk, I will introduce the basic concepts of optimal transportation theory as well as some algorithmic ideas that were proposed in machine learning based on it. These algorithmic ideas include a recently proposed unsupervised learning algorithm based on regularized optimal transport and a new method for feature selection applied in the context of domain adaptation for prostate cancer mapping. From that point I will further present some theoretical insights that were obtained based on the optimal transportation theory for domain adaptation in order to justify its use in this context and to analyze the a priori success of adaptation represented by the existence of the joint hypothesis between source and target domains.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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