Customer-obsessed science
Research areas
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December 1, 20258 min read“Network language models” will coordinate complex interactions among intelligent components, computational infrastructure, access points, data centers, and more.
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November 20, 20254 min read
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October 20, 20254 min read
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October 14, 20257 min read
Featured news
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SAT 20222022We present approaches to store and restore the state of a SAT solver, allowing us to migrate the state between different compute resources, or even between different solvers. This can be used in many ways, e.g., to improve the fault tolerance of solvers, to schedule SAT problems on a restricted number of cores, or to use dedicated preprocessing tools for in-processing. We identify a minimum viable subset
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SIGIR 20222022Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions. To overcome
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EMAC 20222022Brands are searching for innovative ways to reach customers online. Sponsored Display (SD) by Amazon Ads is a new way to do so, and allows customer reaching strategy by category, product and audience. However, advertisers are uncertain how much SD improves their performance over different time horizons. This paper studies more than 40,000 brands with two different methods: a diffusion-regression state-space
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ACM FAccT 20222022We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time — in other words, how can we train a model to be fair by race when we don’t have data about race? We adopt a fairness pipeline perspective, in which an “upstream” learner that does have access to the sensitive features will learn a proxy model for these features
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ACM FAccT 20222022We propose and analyze an algorithmic framework for “bias bounties” — events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security. Our framework allows participants to submit arbitrary subgroup improvements, which are then algorithmically incorporated into an updated model. Our algorithm has the property that there is no
Collaborations
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