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Automated method helps researchers quantify uncertainty in their predictions
An easy-to-use technique could assist everyone from economists to sports analysts, outlined in new paper from team including SDSC faculty Tamara Broderick.
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How symmetry can come to the aid of machine learning
MIT researchers including SDSC faculty Stefanie Jegelka show that exploiting the symmetry within datasets can decrease the amount of data needed for training neural networks.
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“MIT can give you ‘superpowers’”
MITx MicroMasters credential leads learner to accelerated graduate program in data science.
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Automated system teaches users when to collaborate with an AI assistant
MIT researchers including paper lead author and SES/IDPS student Hussein Mozannar develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.
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AI accelerates problem-solving in complex scenarios
A new, data-driven approach developed by researchers including SES/IDPS graduate student Sirui Li could lead to better solutions for tricky optimization problems like global package routing or power grid operation
