Their primary objective is to efficiently support the insertion of new elements with assigned priorities and the extraction of the highest priorityelement.

We use decision trees to convey this reasoning information, as they can be easily represented in natural language, effectively providing knowledge from prior experiments ( i.e., the impact of the generated features on performance) to

In recommender systems, the collected data used for training is always subject to selection bias, which poses a great challenge for unbiased learning.

Specifically, we consider an individual calibration objective for characterizing the quantiles of the prediction model.

Code is available at https://github.com/wl-zhao/UniPC . In this paper, we propose a training-free framework for the fast sampling of DPMs called UniPC .

Several data valuation algorithms have been proposed to quantify data quality, however, there lacks a systemic and standardized benchmarking system for data valuation.

We study the problem of combining neural networks with symbolic reasoning.