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Time: 2:00-3:30 PM, Monday, April 15thPlace: CSE 1202
Title: Controlling the Black Box: Learning Manipulable and Fair Representations
Abstract: Machine learning models, and more specifically deep neural networks, are achieving state-of-the-art performance on difficult pattern-recognition tasks such as object recognition, speech recognition, drug discovery, and more. However, deep networks are notoriously difficult to understand, both in how they arrive at and how to affect their responses. As these systems become more prevalent in real-world applications it is essential to allow users to exert more control over the learning system. In particular a wide range of applications can be facilitated by exerting some structure over the learned representations, to enable users to manipulate, interpret, and in some cases obfuscate the representations. In this talk I will discuss recent work that makes some steps towards these goals, allowing users to interact with and control representations.
Bio: Richard Zemel is a Professor of Computer Science and Industrial Research Chair in Machine Learning at the University of Toronto, and the co-founder and the Research Director at the Vector Institute for Artificial Intelligence. Prior to that he was on the faculty at the University of Arizona, and a Postdoctoral Fellow at the Salk Institute and at CMU. He received the B.Sc. in History & Science from Harvard, and a Ph.D. in Computer Science from the University of Toronto. He is also the co-founder of SmartFinance, a financial technology startup specializing in data enrichment and natural language processing. His awards and honors include a Young Investigator Award from the ONR and a US Presidential Scholar award. He is a Senior Fellow of the Canadian Institute for Advanced Research, an NVIDIA Pioneer of AI, and a member of the NeurIPS Advisory Board. His research is supported by grants from NSERC, CIFAR, Google, Samsung, Amazon, Microsoft, DARPA and iARPA.