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Reliable Machine Learning in the Wild
Dylan Hadfield-Menell · Jacob Steinhardt · Adrian Weller · Smitha Milli

Thu Aug 10 03:30 PM -- 12:30 AM (PDT) @ C4.7
Event URL: https://sites.google.com/site/wildml2017icml/ »

When can we trust that a system that has performed well in the past will continue to do so in the future? Designing systems that are reliable in the wild is essential for high stakes applications such as self-driving cars and automated surgical assistants. This workshop aims to bring together researchers in diverse areas such as reinforcement learning, human-robot interaction, game theory, cognitive science, and security to further the field of reliability in machine learning. We will focus on three aspects — robustness (to adversaries, distributional shift, model misspecification, corrupted data); awareness (of when a change has occurred, when the model might be miscalibrated, etc.);and adaptation (to new situations or objectives). We aim to consider each of these in the context of the complex human factors that impact the successful application or meaningful monitoring of any artificial intelligence technology. Together, these will aid us in designing and deploying reliable machine learning systems.

Author Information

Dylan Hadfield-Menell (University of California, Berkeley)
Jacob Steinhardt (Stanford University)
Adrian Weller (University of Cambridge, Alan Turing Institute)
Adrian Weller

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

Smitha Milli (UC Berkeley)

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