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supporter of Google, in addition to many other successful startups and venture firms in Silicon Valley. Strategic Classification from Revealed Preferences. Lp Testing and Learning of Discrete Distributions (itcs 2015) Bo Waggoner Examines uniformity testing and learning of discrete materialism essay distributions, given access to independent samples, under p metrics. Considers voting when voters might cast multiple votes false-name manipulation. In general, gives upper and lower bounds on number of samples needed (tight everywhere for learning and in many cases for uniformity testing). This can give drastic improvements for eliciting some statistics that have large "elicitation complexity".

Preserves differential privacy of contributions. Slides (pdf), slides with notes (pdf). Toward Buying Labels from the Crowd. This paper formalizes a model for this process and investigates settings where efficient algorithms can be used. Informational Substitutes: Definitions and Design. (aaai 2015) Yiling Chen, Kobbi Nissim, and Bo Waggoner Designs a mechanism for the following problem: A group of agents (e.g.

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Soda 2015, San Diego,. CiML Workshop at nips, Long Beach,. Slides (pdf), slides and notes (pdf). He received the Gödel Prize in 2001 for his research on probabilistically checkable proofs and hardness of approximation. Evaluating Resistance to False-Name Manipulations in Elections (aaai 2012) Bo Waggoner, Lirong Xia, and Vincent Conitzer. We consider algorithms for parsimoniously drawing samples and solving the optimization problem. Young Researchers -. Rajeev Motwani, former Professor, stanford University rajeevtacstdstanfordtdedu, rajeev Motwani was born research scholarly paper on March 24, 1962 in Jammu, India. The approximation ratio.534. Georgetown Computer Science, Washington,.C.

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