Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Causal reasoning and logical reasoning are two important types of reasoning abilities for
human intelligence. However, their relationship has not been extensively explored under …

This paper presents ExplainableFold (xFold), which is an Explainable AI framework for
protein structure prediction. Despite the success of AI-based methods such as AlphaFold …

Human intelligence is able to first learn some basic skills for solving basic problems and
then assemble such basic skills into complex skills for solving complex or new problems. For …

We present GRACIE (Graph Recalibration and Adaptive Counterfactual Inspection and
Explanation), a novel approach for generative classification and counterfactual explanations …

Given the complexity and lack of transparency in deep neural networks (DNNs), extensive
efforts have been made to make these systems more interpretable or explain their behaviors …

This paper describes limits of human intelligence and how humans invented artificial
intelligence and machine learning. It explores the paradoxical situation that the tools that …

Data-driven equation identification for dynamical systems has achieved great progress,
which for static systems, however, has not kept pace. Unlike dynamical systems, static …

Recommender systems (RS)—which are extensively deployed in various systems such as e-
commerce, social networks, search engines, news portals, hiring platforms, intelligent …

Discovering non-linear dynamical models from data is at the core of science. Recent
progress hinges upon sparse regression of observables using extensive libraries of …