Demo Data from EDBT 2026 / reviewers in the wild / expert
Elton Yechao Zhu
dblp:199/2257
· DBLP profile ↗
3ranked-venue papers
2as first author
1since 2021 · last 2025
0000-0002-4497-2093ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Theory of computation · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 |
Quantum computing and quantum information · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
interpretability |
0.9 | 1 | 2025 | BoolXAI: Explainable AI Using Expressive Boolean Formulas · AAAI 2025 |
Machine learning › Trustworthy machine learning › interpretability
post-hoc explanation |
0.9 | 1 | 2025 | BoolXAI: Explainable AI Using Expressive Boolean Formulas · AAAI 2025 |
Quantum computing and quantum information
quantum channel |
0.4 | 1 | 2019 | Superadditivity in Trade-Off Capacities of Quantum Channels · IEEE Trans. Inf. Theory 2019 |
Quantum computing and quantum information › quantum channel capacity
superadditivity |
0.4 | 1 | 2019 | Superadditivity in Trade-Off Capacities of Quantum Channels · IEEE Trans. Inf. Theory 2019 |
Methods — techniques the papers use, named apart from their topics
local optimization · 0.9boolean formula learning · 0.9quantum channel capacity · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | BoolXAI: Explainable AI Using Expressive Boolean FormulasabstractIn this tool paper, we design, develop, and release BoolXAI, an interpretable machine learning classification approach for Explainable AI (XAI) based on expressive Boolean formulas. The Boolean formula defines a logical rule with tunable complexity according to which input data are classified. Beyond the classical conjunction and disjunction, BoolXAI offers expressive operators such as AtLeast, AtMost, and Choose and their parameterization. This provides higher expressiveness compared to rigid rules- and tree-based approaches. We show how to train BoolXAI classifiers effectively using native local optimization to search the space of feasible formulas. We provide illustrative results on several well-known public benchmarks that demonstrate the competitive nature of our approach compared to existing methods. Our work is embodied in the open-source BoolXAI library with a high-level user interface to serve researchers and practitioners. BoolXAI can be used either as a standalone interpretable classifier or for post-hoc explanations of other black-box models or observed behavior. We highlight several desirable benefits of our tool, especially in industrial settings where rapid experimentation, reusability, reproducibility, deployment, and maintenance are of great interest. Finally, we showcase a deployed service powered by BoolXAI as an enterprise application. Serdar Kadioglu, Elton Yechao Zhu, Gili Rosenberg, John Kyle Brubaker, Martin J. A. Schuetz, Grant Salton, Zhihuai Zhu, Helmut G. Katzgraber |
AAAI | 2 |
| 2019 | Superadditivity in Trade-Off Capacities of Quantum Channels
Elton Yechao Zhu, Quntao Zhuang, Min-Hsiu Hsieh, Peter W. Shor |
IEEE Trans. Inf. Theory | 1 |
| 2018 | Superadditivity in Trade-Off Capacities of Quantum ChannelsabstractIn this paper, we investigate the additivity phenomenon in the quantum dynamic capacity region of a quantum channel for trading the resources of classical communication, quantum communication, and entanglement. Understanding such an additivity property is important if we want to optimally use a quantum channel for general communication purposes. However, in a lot of cases, the channel one will be using only has an additive single or double resource capacity region, and it is largely unknown if this could lead to a strictly superadditive double or triple resource capacity region, respectively. For example, if a channel has additive classical and quantum capacities, can the classical-quantum capacity region be strictly superadditive? In this paper, we answer such questions affirmatively. We give proof-of-principle requirements for these channels to exist. In most cases, we can provide an explicit construction of these quantum channels. The existence of these superadditive phenomena is surprising in contrast to the result that the additivity of both classical-entanglement and classical-quantum capacity regions imply the additivity of the triple resource capacity region for a given channel. Elton Yechao Zhu, Quntao Zhuang, Min-Hsiu Hsieh, Peter W. Shor |
ISIT | 1 |