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pro vyhledávání: '"Jenkins, Porter"'
Neural networks that can produce accurate, input-conditional uncertainty representations are critical for real-world applications. Recent progress on heteroscedastic continuous regression has shown great promise for calibrated uncertainty quantificat
Externí odkaz:
http://arxiv.org/abs/2406.09262
Product assortment selection is a critical challenge facing physical retailers. Effectively aligning inventory with the preferences of shoppers can increase sales and decrease out-of-stocks. However, in real-world settings the problem is challenging
Externí odkaz:
http://arxiv.org/abs/2406.07769
While significant progress has been made in specifying neural networks capable of representing uncertainty, deep networks still often suffer from overconfidence and misaligned predictive distributions. Existing approaches for addressing this misalign
Externí odkaz:
http://arxiv.org/abs/2405.12412
In practice, it is essential to compare and rank candidate policies offline before real-world deployment for safety and reliability. Prior work seeks to solve this offline policy ranking (OPR) problem through value-based methods, such as Off-policy e
Externí odkaz:
http://arxiv.org/abs/2312.11551
Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect consumers with products. However, selecting product locations within a s
Externí odkaz:
http://arxiv.org/abs/2001.03210
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