The collected data in recommender systems generally suffers selection bias. Considerable works are proposed to address selection bias induced by observed user and item features, but they fail when hidden features (e.g., user age or salary) that affect

Neural Information Processing Systems http://nips.cc/

We find that the ability of LLMs to maintain consistency varies across different moral scenarios.

Neural Information Processing Systems http://nips.cc/

In this problem, the learner is given observational data - a set of examples selected according to some policy along with their labels - as well as access to the policy that selects the examples, and the goal is to construct a classifier with high performance on an entire population, notjusttheobservational data distribution.

Mitigating these biases relies on exposing and removingnuisance components.