Tag Archive: machine learning

Wide-Area Data Analytics

/, //CRA/CCC
Modern datasets are often distributed across many locations. This workshop will bring together researchers and practitioners in the database, networking, distributed systems, and storage fields in order to bridge the gap in research within wide-area data analytics.
Fairness and Accountability Task Force will hold a visioning workshop on Economics and Fairness, May 22-23, 2019 in Cambridge, Massachusetts. This workshop will bring together computer science researchers with backgrounds in algorithmic decision making, machine learning, and data science with policy makers, legal experts, economists, and business leaders to discuss methods to ensure economic fairness in a data-driven world. '>

Economics and Fairness

/, , //Cambridge MA
The Computing Community Consortium's (CCC) Fairness and Accountability Task Force will hold a visioning workshop on Economics and Fairness, May 22-23, 2019 in Cambridge, Massachusetts. This workshop will bring together computer science researchers with backgrounds in algorithmic decision making, machine learning, and data science with policy makers, legal experts, economists, and business leaders to discuss methods to ensure economic fairness in a data-driven world.

Cyber-Social Learning Systems Workshop 3

/, //Washington, DC
Over the last decade, we have made enormous progress establishing scientific and engineering principles for cyber-physical systems (CPS). The next major frontier in science and engineering research and development, is the integration of cyber-physical with human and social systems and phenomena at all scales. Closing the loop from sensing to performance at all scales will give rise to cyber-social learning systems. This is part of a workshop series – view the series page.

Cyber-Social Learning Systems Workshop 2

/, //Graduate Hotel, Ann Arbor
Over the last decade, we have made enormous progress establishing scientific and engineering principles for cyber-physical systems (CPS). The next major frontier in science and engineering research and development, is the integration of cyber-physical with human and social systems and phenomena at all scales. Closing the loop from sensing to performance at all scales will give rise to cyber-social learning systems. This is part of a workshop series – view the series page.
Jerry Zhu giving a lecture.

Machine Teaching

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Machine teaching is machine learning turned upside down: it is about finding the optimal (e.g. the smallest) training set. For example, consider a “student” who runs the Support Vector Machine learning algorithm. Imagine a teacher who wants to teach the student a specific target hyperplane in some feature space (never mind how the teacher got this hyperplane in the first place). The teacher constructs a training set D=(x1,y1) … (xn, yn), where xi is a feature vector and yi a class label, to train the student. What is the smallest training set that will make the student learn the target hyperplane?