7 citations · 17 across the 6 of their papers we have counts for
7 papers
From Failure Taxonomy to Intervention: A Diagnostic Methodology for Industry-Scale AVLM in Video and Live-Streaming Platform Moderation
Shuchang Ye, Jinqiang Yu, Zhujun Xiao +6
Industry-scale video and live-streaming moderation imposes requirements that are difficult to satisfy with generic pretrained public models or external APIs, including adaptation t…
Formal Explanations for Neuro-Symbolic AI
Sushmita Paul, Jinqiang Yu, Jip J. Dekker +2
Despite the practical success of Artificial Intelligence (AI), current neural AI algorithms face two significant issues. First, the decisions made by neural architectures are often…
Anytime Approximate Formal Feature Attribution
Jinqiang Yu, Graham Farr, Alexey Ignatiev +1
Widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models on the one hand and a number of crucial issues pertaining to them warrant the need for ex…
On Formal Feature Attribution and Its Approximation
Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey
Recent years have witnessed the widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models. Despite their tremendous success, a number of vital prob…
Eliminating The Impossible, Whatever Remains Must Be True
Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey +2
The rise of AI methods to make predictions and decisions has led to a pressing need for more explainable artificial intelligence (XAI) methods. One common approach for XAI is to pr…
Optimal Decision Lists using SAT
Jinqiang Yu, Alexey Ignatiev, Pierre Le Bodic +1
Decision lists are one of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, this machine learning model is…
Computing Optimal Decision Sets with SAT
Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey +1
As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use…