Strategyproof linear regression in high dimensions
نویسندگان
چکیده
منابع مشابه
Strategyproof Linear Regression in High Dimensions
is paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources. Specically, we focus on the ubiquitous problem of linear regression, where strategyproof mechanisms have previously been identied in two dimensions. Our main contribution is the discovery of...
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Designing machine learning algorithms that are robust to noise in training data has lately been a subject of intense research. A large body of work addresses stochastic noise [12, 7], while another one studies adversarial noise [11, 2] in which errors are introduced by an adversary with the explicit purpose of sabotaging the algorithm. This is often too pessimistic, and leads to negative result...
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ژورنال
عنوان ژورنال: ACM SIGecom Exchanges
سال: 2019
ISSN: 1551-9031
DOI: 10.1145/3331033.3331038