Designing AI for Racial Equity: Translating Ethics into Practice
E18-304Please join us on Monday, November 1, 2021 at 4:00pm for the Distinguished Speaker Seminar with S. Craig Watkins (MLK Visiting Professor).
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Revealing the simplicity of high-dimensional objects via pathwise analysis
E18-304Abstract: One of the main reasons behind the success of high-dimensional statistics and modern machine learning in taming the curse of dimensionality is that many classes of high-dimensional distributions are surprisingly well-behaved and, when viewed correctly, exhibit a simple structure. This emergent simplicity is in the center of the theory of "high-dimensional phenomena", and is manifested in principles such as "Gaussian-like behavior" (objects of interest often inherit the properties of the Gaussian measure), "dimension-free behavior" (expressed in inequalities which do…
Instance Dependent PAC Bounds for Bandits and Reinforcement Learning
E18-304Abstract: The sample complexity of an interactive learning problem, such as multi-armed bandits or reinforcement learning, is the number of interactions with nature required to output an answer (e.g., a recommended arm or policy) that is approximately close to optimal with high probability. While minimax guarantees can be useful rules of thumb to gauge the difficulty of a problem class, algorithms optimized for this worst-case metric often fail to adapt to “easy” instances where fewer samples suffice. In this talk, I…
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