Congrats to Weida Li @ZhuanghuaL Yaoliang Yu for receiving the Best Paper - Runner-up Award at #UAI2026 @UncertaintyInAI for our work "Nonlinear Axiomatic Attribution for Cooperative Games"!!! See what Weida has to say about our work below: Since digging into the #ShapleyValue, one question has lingered in my mind: To what extent are the Shapley value and its counterparts actually useful in AI? For simplicity, we focus on the AUC metric, the area under the insertion curve, which is commonly used across scenarios to identify the combination of players with the most contributions. We treat this metric as a must-use benchmark to examine the Shapley value and its counterparts, and study how well they perform at maximizing this AUC metric. As far as we know, the popularity of the Shapley value stems from it being the unique attribution method satisfying a certain set of axioms. However, several works have theoretically demonstrated its unreliability in different aspects. They tell the same story: The linearity axiom makes the Shapley value a linear operator with a large null space. As a result, there exist many significantly different cooperative games (also called utility/characteristic functions) that share the same Shapley value, i.e., the Shapley value can't distinguish between them. This argument also extends to the AUC metric. Since the AUC metric only concerns the ranking of assigned contributions, the efficiency axiom is unnecessary. This raises a question: What would attribution methods look like if we remove only the linearity and efficiency axioms? The mathematical convenience that the linearity axiom provides is significant. It allows us to break down a cooperative game into a sum of easy-to-tackle simple games as the first step. Without linearity, it's a different story. Still, in our UAI2026 paper, we introduced a class of nonlinear axiomatic attribution methods through additive approximation. Concurrently, and as something of a happy coincidence, we discovered another class of nonlinear axiomatic attribution methods in "TreeGrad-Ranker: Feature Ranking via O(L)-Time Gradients for Decision Trees", which is accepted to @iclr_conf #ICLR2026. It was derived by heuristically maximizing the AUC metric, and the resulting formula resembles the Shapley value, except that the weights now depend on the cooperative game itself. If you find our UAI2026 work interesting, this one is definitely worth a read too! It is worth mentioning that our ICLR2026 work also led to a small contribution to the great #XGBoost library, which had just won the Test of Time Award @kdd_news #KDD2026! In our research, we wanted utility functions that are sufficiently complex while still allowing the #ShapleyValue and its counterparts to be computed exactly without approximation. That search led us to the linear TreeSHAP algorithm where we noticed a severe numerical issue and introduced a fix, TreeGrad-SHAP, which was later adopted and efficiently implemented into the #XGBoost library through the excellent work "Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactions".