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Chunxi Ji

dblp:317/0861 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2023
0000-0002-4475-1987ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Theory of computation · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Trustworthy machine learning · 100%
Theoretical computer science
1 paper
Automated reasoning and model checking · 77% Mathematical optimization · 23%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › interpretability › local explanation
contrastive explanation
0.612022
On the Computation of Necessary and Sufficient Explanations · AAAI 2022
Machine learning › Trustworthy machine learning › interpretability › model explanation
decision explanation
0.612022
On the Computation of Necessary and Sufficient Explanations · AAAI 2022
Mathematical optimization › discrete optimization › boolean function minimization
prime implicant
0.212022
On the Computation of Necessary and Sufficient Explanations · AAAI 2022

Methods — techniques the papers use, named apart from their topics

sentential decision diagrams · 1.1
YearPublicationVenuePosition
2023 A New Class of Explanations for Classifiers with Non-binary Features
Chunxi Ji, Adnan Darwiche
JELIA1
2022 On the Computation of Necessary and Sufficient Explanations
abstract
The complete reason behind a decision is a Boolean formula that characterizes why the decision was made. This recently introduced notion has a number of applications, which include generating explanations, detecting decision bias and evaluating counterfactual queries. Prime implicants of the complete reason are known as sufficient reasons for the decision and they correspond to what is known as PI explanations and abductive explanations. In this paper, we refer to the prime implicates of a complete reason as necessary reasons for the decision. We justify this terminology semantically and show that necessary reasons correspond to what is known as contrastive explanations. We also study the computation of complete reasons for multi-class decision trees and graphs with nominal and numeric features for which we derive efficient, closed-form complete reasons. We further investigate the computation of shortest necessary and sufficient reasons for a broad class of complete reasons, which include the derived closed forms and the complete reasons for Sentential Decision Diagrams (SDDs). We provide an algorithm which can enumerate their shortest necessary reasons in output polynomial time. Enumerating shortest sufficient reasons for this class of complete reasons is hard even for a single reason. For this problem, we provide an algorithm that appears to be quite efficient as we show empirically.
Adnan Darwiche, Chunxi Ji
AAAI2