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Distribution shifts: connecting methods and applications (DistShift)
Shiori Sagawa · Pang Wei Koh · Fanny Yang · Hongseok Namkoong · Jiashi Feng · Kate Saenko · Percy Liang · Sarah Bird · Sergey Levine

Mon Dec 13 05:00 AM -- 05:00 PM (PST) @ None
Event URL: https://sites.google.com/view/distshift2021 »

Distribution shifts---where a model is deployed on a data distribution different from what it was trained on---pose significant robustness challenges in real-world ML applications. Such shifts are often unavoidable in the wild and have been shown to substantially degrade model performance in applications such as biomedicine, wildlife conservation, sustainable development, robotics, education, and criminal justice. For example, models can systematically fail when tested on patients from different hospitals or people from different demographics. Despite the ubiquity of distribution shifts in ML applications, work on these types of real-world shifts is currently underrepresented in the ML research community, with prior work generally focusing instead on synthetic shifts. However, recent work has shown that models that are robust to one kind of shift need not be robust to another, underscoring the importance and urgency of studying the types of distribution shifts that arise in real-world ML deployments. With this workshop, we aim to facilitate deeper exchanges between domain experts in various ML application areas and more methods-oriented researchers, and ground the development of methods for characterizing and mitigating distribution shifts in real-world application contexts.

Author Information

Shiori Sagawa (Stanford University)
Pang Wei Koh (Stanford University)
Fanny Yang (ETH)
Hongseok Namkoong (Ass Prof Columbia)
Jiashi Feng (National University of Singapore)
Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)
Percy Liang (Stanford University)
Sarah Bird (Microsoft)

Sarah’s work focuses on research and emerging technology strategy for AI products in Azure. Sarah works to accelerate the adoption and positive impact of AI by bringing together the latest innovations in research with the best of open source and product expertise to create new tools and technologies. Sarah is currently leading Responsible AI for the Azure Cognitive Services. Prior to joining the Cognitive Services, Sarah lead the development of responsible AI tools in Azure Machine Learning. She is an active member of the Microsoft AETHER committee, where she works to develop and drive company-wide adoption of responsible AI principles, best practices, and technologies. Sarah was one of the founding researchers in the Microsoft FATE research group and prior to joining Microsoft worked on AI fairness in Facebook. Sarah is active contributor to the open source ecosystem, she co-founded ONNX, Fairlearn, and OpenDP’s SmartNoise was a leader in the Pytorch 1.0 and InterpretML projects. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She co-founded the MLSys research conference and the Learning Systems workshops. She has a Ph.D. in computer science from UC Berkeley advised by Dave Patterson, Krste Asanovic, and Burton Smith.

Sergey Levine (UC Berkeley)

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