Opponent
发表于 2025-3-25 07:08:17
Conference proceedings 2020ing and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track..
frenzy
发表于 2025-3-25 08:43:14
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胆大
发表于 2025-3-25 12:24:11
Deep Ordinal Reinforcement Learningibit a performance that is comparable to the numerical variations for a number of problems. We also give first evidence that our ordinal variant is able to produce better results for problems with less engineered and simpler-to-design reward signals.
corporate
发表于 2025-3-25 18:46:57
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微不足道
发表于 2025-3-25 21:49:55
Stochastic One-Sided Full-Information Bandit the mean reward of arms and the mean reward of the best arm, and . is a formula depending on the gap vector that we will specify in detail. Our algorithm has the best theoretical regret upper bound so far. We also validate our algorithm empirically against other possible alternatives.
stroke
发表于 2025-3-26 03:24:38
A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournamentt (e.g., clay or hard court) is a key determinant of the performances of male players, but less so for females. Top players on various surfaces over this longitudinal period are also identified in an objective manner.
Intercept
发表于 2025-3-26 06:15:33
An Engineered Empirical Bernstein Boundce information. We illustrate the practical usefulness of our novel EBB by applying it to a multi-armed bandit problem as a component of a UCB method. Our method outperforms existing approaches by producing lower expected regret than variants of UCB employing several other bounds, including state-of-the-art EBBs.
Oratory
发表于 2025-3-26 12:32:10
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FLAT
发表于 2025-3-26 15:59:13
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莎草
发表于 2025-3-26 17:49:42
A Reduction of Label Ranking to Multiclass Classificationions. We discuss theoretical properties of the proposed method in terms of accuracy, error correction, and computational complexity. Experimental results are promising and indicate that improvements upon the special case of pairwise preference decomposition are indeed possible.