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.