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Provable benefits of policy learning from human preferences in contextual bandit problems
For a real-world decision-making problem, the reward function often needs to be engineered
or learned. A popular approach is to utilize human feedback to learn a reward function for …
or learned. A popular approach is to utilize human feedback to learn a reward function for …
Crowdsourced top-k queries by pairwise preference judgments with confidence and budget control
Crowdsourced query processing is an emerging technique that tackles computationally
challenging problems by human intelligence. The basic idea is to decompose a …
challenging problems by human intelligence. The basic idea is to decompose a …
Efficient crowdsourced best objects finding via superiority probability based ordering for decision support systems
B Yin, W Zeng, X Wei - Expert Systems with Applications, 2023 - Elsevier
Best objects finding is a fundamental operation in decision support systems and
applications. When numerical values of objects cannot be obtained from existing computer …
applications. When numerical values of objects cannot be obtained from existing computer …
Learning from ranking data: theory and methods
A Korba - 2018 - pastel.hal.science
Ranking data, ie, ordered list of items, naturally appears in a wide variety of situations,
especially when the data comes from human activities (ballots in political elections, survey …
especially when the data comes from human activities (ballots in political elections, survey …
15 Generalized Low-Rank Optimization for Ultra-dense Fog-RANs
Expectations for new wireless networks have become higher since mobile data has grown
exponentially and more diverse user services have emerged. Intensive deployment of …
exponentially and more diverse user services have emerged. Intensive deployment of …