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Some Challenging Machine Learning Problems in Computational Biology: Time-Varying Networks Inference and Sparse Structured Input-Out Learning

Some Challenging Machine Learning Problems in Computational Biology: Time-Varying Networks Inference and Sparse Structured Input-Out Learning

This video was recorded at Carnegie Mellon Machine Learning Lunch seminar. Recent advances in high-throughput technologies such as microarrays and genome-wide sequencing have led to an avalanche of new biological data that are dynamic, noisy, heterogeneous, and high-dimensional. They have raised unprecedented challenges in machine learning and high-dimensional statistical analysis; and their close relevance to human health and social welfare has often created unique demands on performance metric different from standard data mining or pattern recognition problems. In this talk, I will discuss two of such problems. First, I will present a new statistical formalism for modeling network evolution over time, and several new algorithms based on temporal extensions of the sparse graphical logistic... Show More

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