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Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification
Alkis Gotovos · Rebekka Burkholz · John Quackenbush · Stefanie Jegelka

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @ None #None

Modeling the time evolution of discrete sets of items (e.g., genetic mutations) is a fundamental problem in many biomedical applications. We approach this problem through the lens of continuous-time Markov chains, and show that the resulting learning task is generally underspecified in the usual setting of cross-sectional data. We explore a perhaps surprising remedy: including a number of additional independent items can help determine time order, and hence resolve underspecification. This is in sharp contrast to the common practice of limiting the analysis to a small subset of relevant items, which is followed largely due to poor scaling of existing methods. To put our theoretical insight into practice, we develop an approximate likelihood maximization method for learning continuous-time Markov chains, which can scale to hundreds of items and is orders of magnitude faster than previous methods. We demonstrate the effectiveness of our approach on synthetic and real cancer data.

Author Information

Alkis Gotovos (MIT CSAIL)
Rebekka Burkholz (Harvard University)
John Quackenbush Quackenbush (Harvard T.H. Chan School of Public Health)

John Quackenbush is Professor of Computational Biology and Bioinformatics and Chair of the Department of Biostatistics at the Harvard TH Chan School of Public Health, Professor in the Channing Division of Network Medicine, and Professor in the Department of Data Science at the Dana-Farber Cancer Institute. John’s PhD was in Theoretical Physics, but in 1992 he received a fellowship to work on the Human Genome Project. This led him through the Salk Institute, Stanford, The Institute for Genomic Research (TIGR), and to Harvard in 2005. John uses massive data to probe how many small effects combine to influence human health and disease. He has more than 310 scientific papers and over 81,000 citations. Among his honors is recognition in 2013 as a White House Open Science Champion of Change.

Stefanie Jegelka (MIT)

Stefanie Jegelka is an X-Consortium Career Development Assistant Professor in the Department of EECS at MIT. She is a member of the Computer Science and AI Lab (CSAIL), the Center for Statistics and an affiliate of the Institute for Data, Systems and Society and the Operations Research Center. Before joining MIT, she was a postdoctoral researcher at UC Berkeley, and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems. Stefanie has received a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, the German Pattern Recognition Award and a Best Paper Award at the International Conference for Machine Learning (ICML). Her research interests span the theory and practice of algorithmic machine learning.

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