Personal Assistant Systems
The Lawyer Pushing to Protect Future Generations from the Climate Crisis
Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. During the summer of 2006, while pregnant with her son, Julia Olson staggered through a then record-breaking heat wave in Oregon, as New Orleans was just beginning its long road to recovery after Hurricane Katrina hit the year before. At the time, Olson was a public interest environmental lawyer.
This Young Advocate Is Fighting to Make Every School Allergy-Safe
Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. In 2019, 6-year-old Zacky Muñoz was eating his usual lunch of pasta, salad, and breadsticks at his school cafeteria in Pasadena, California. "I suddenly felt a weird feeling--it was like a fight-or-flight response, an alarm inside my body telling me I was in danger," he says.
Trump Says Netanyahu Has to Be 'More Responsible With Respect to Lebanon'
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Gavin Newsom Says Trump DOJ Is Politically Targeting Him
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The Pain and Promise of Summer Camp
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'A Disaster': Iran's World Cup Overshadowed by War With Host U.S.
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AI Doesn't Feel. So Why Does It Have Something Like Emotions?
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U.K.'s Ban on Palestine Action Under Terror Legislation Was Lawful, Court of Appeal Rules
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The Resurgence of Bracero Logic
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Unveiling Extraneous Sampling Bias with Data Missing-Not-At-Random
Selection bias poses a widely recognized challenge for unbiased evaluation and learning in many industrial scenarios. For example, in recommender systems, it arises from the users' selective interactions with items. Recently, doubly robust and its variants have been widely studied to achieve debiased learning of prediction models, however, all of them consider a simple exact matching scenario, i.e., the units (such as user-item pairs in a recommender system) are the same between the training and test sets. In practice, there may be limited or even no overlap in units between the training and test. In this paper, we consider a more practical scenario: the joint distribution of the feature and rating is the same in the training and test sets. Theoretical analysis shows that the previous DR estimator is biased even if the imputed errors and learned propensities are correct in this scenario. In addition, we propose a novel super-population doubly robust estimator (SuperDR), which can achieve a more accurate estimation and desirable generalization error bound compared to the existing DR estimators, and extend the joint learning algorithm for training the prediction and imputation models. We conduct extensive experiments on three real-world datasets, including a large-scale industrial dataset, to show the effectiveness of our method.