Demo Data from VLDB 2026 / reviewers in the wild / expert
Chunxi Ji
dblp:317/0861
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › interpretability › local explanation
contrastive explanation |
0.6 | 1 | 2022 | On the Computation of Necessary and Sufficient Explanations · AAAI 2022 |
Machine learning › Trustworthy machine learning › interpretability › model explanation
decision explanation |
0.6 | 1 | 2022 | On the Computation of Necessary and Sufficient Explanations · AAAI 2022 |
Mathematical optimization › discrete optimization › boolean function minimization
prime implicant |
0.2 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A New Class of Explanations for Classifiers with Non-binary Features
Chunxi Ji, Adnan Darwiche |
JELIA | 1 |
| 2022 | On the Computation of Necessary and Sufficient ExplanationsabstractThe 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 |
AAAI | 2 |