EDBT 2026 Demo / reviewers in the wild / expert
Shi-Bing Li
dblp:390/1316
· DBLP profile ↗
3ranked-venue papers
3as first author
3since 2021 · last 2026
0009-0005-0606-9521ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 2 · 2 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 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.
| Theoretical computer science
2 papers |
Information theory · 64% Coding theory · 36% | |
| Network and information security
1 paper |
Privacy and data protection · 100% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection › differential privacy
privacy amplification |
1.0 | 1 | 2026 | Two-Parameter Rényi Information Quantities With Applications to Privacy Amplification and Soft Covering · IEEE Trans. Inf. Theory 2026 |
Coding theory › channel coding
channel simulation |
1.0 | 1 | 2026 | Large Deviation Analysis for the Reverse Shannon Theorem · IEEE Trans. Inf. Theory 2026 |
Information theory › probability theory
large deviations |
1.0 | 1 | 2026 | Large Deviation Analysis for the Reverse Shannon Theorem · IEEE Trans. Inf. Theory 2026 |
Coding theory › channel coding › error exponent
reliability function |
1.0 | 1 | 2026 | Large Deviation Analysis for the Reverse Shannon Theorem · IEEE Trans. Inf. Theory 2026 |
Information theory › channel capacity
reverse shannon theorem |
1.0 | 1 | 2026 | Large Deviation Analysis for the Reverse Shannon Theorem · IEEE Trans. Inf. Theory 2026 |
Information theory › information measures › divergence measures
rényi divergence |
0.3 | 1 | 2026 | Large Deviation Analysis for the Reverse Shannon Theorem · IEEE Trans. Inf. Theory 2026 |
Information theory
soft covering |
0.3 | 1 | 2026 | Two-Parameter Rényi Information Quantities With Applications to Privacy Amplification and Soft Covering · IEEE Trans. Inf. Theory 2026 |
Methods — techniques the papers use, named apart from their topics
rényi divergence · 3.0privacy amplification · 2.0large deviation analysis · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Two-Parameter Rényi Information Quantities and Their Applications
Shi-Bing Li |
ISIT | 1 |
| 2026 | Large Deviation Analysis for the Reverse Shannon TheoremabstractWe consider the problem of simulating a noisy channel using noiseless channels with unlimited shared randomness. This can be interpreted as the reverse problem of Shannon’s noisy channel coding theorem. In contrast to previous works, we employ Rényi divergence (with the parameter α ∈ [0,∞]) to measure the level of approximation, and we obtain the reverse Shannon theorem under this measure, which characterizes the Rényi simulation rate, the minimum communication rate required for the Rényi divergence vanishing asymptotically. Our derivation is done by a precise large-deviation analysis. When the communication rate is above the Rényi simulation rate, we provide a complete characterization of the convergence exponent for the Rényi divergence, called the reliability function. When the communication rate is below the Rényi simulation rate, we determine the linear increasing rate for the Rényi divergence, which implies the strong converse exponent for the order-α fidelity. Shi-Bing Li, Ke Li 0016, Lei Yu 0003 |
IEEE Trans. Inf. Theory | 1 |
| 2026 | Two-Parameter Rényi Information Quantities With Applications to Privacy Amplification and Soft Covering
Shi-Bing Li, Ke Li 0016, Lei Yu 0003 |
IEEE Trans. Inf. Theory | 1 |