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- W4287072266 abstract "A fundamental challenge for any intelligent system is prediction: given some inputs, can you predict corresponding outcomes? Most work on supervised learning has focused on producing accurate marginal predictions for each input. However, we show that for a broad class of decision problems, accurate joint predictions are required to deliver good performance. In particular, we establish several results pertaining to combinatorial decision problems, sequential predictions, and multi-armed bandits to elucidate the essential role of joint predictive distributions. Our treatment of multi-armed bandits introduces an approximate Thompson sampling algorithm and analytic techniques that lead to a new kind of regret bound." @default.
- W4287072266 created "2022-07-25" @default.
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- W4287072266 date "2021-07-19" @default.
- W4287072266 modified "2023-09-25" @default.
- W4287072266 title "From Predictions to Decisions: The Importance of Joint Predictive Distributions" @default.
- W4287072266 doi "https://doi.org/10.48550/arxiv.2107.09224" @default.
- W4287072266 hasPublicationYear "2021" @default.
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