EDBT 2026 Demo / reviewers in the wild / expert
Lasse Fischer
dblp:430/6729
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2026
0000-0003-3380-0066ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Theory 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.
| Theoretical computer science
1 paper |
Information theory · 67% Coding theory · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory
error-correcting codes |
1.0 | 1 | 2026 | Improving Wald's (Approximate) Sequential Probability Ratio Test by Avoiding Overshoot · IEEE Trans. Inf. Theory 2026 |
Information theory
hypothesis testing |
1.0 | 1 | 2026 | Improving Wald's (Approximate) Sequential Probability Ratio Test by Avoiding Overshoot · IEEE Trans. Inf. Theory 2026 |
Information theory › statistical inference › sequential analysis › sequential detection
sequential probability ratio test |
1.0 | 1 | 2026 | Improving Wald's (Approximate) Sequential Probability Ratio Test by Avoiding Overshoot · IEEE Trans. Inf. Theory 2026 |
Methods — techniques the papers use, named apart from their topics
sequential boosting · 1.0conformal martingales · 1.0confidence sequences · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Improving Wald's (Approximate) Sequential Probability Ratio Test by Avoiding OvershootabstractWald’s sequential probability ratio test (SPRT) is a cornerstone of sequential analysis. Based on desired type-I, II error levels α, β, it stops when the likelihood ratio crosses certain thresholds, guaranteeing optimality of the expected sample size. However, these thresholds are not closed form and the test is often applied with approximate thresholds (1 – β)/α and β/(1 – α) (approximate SPRT). When β > 0, this neither guarantees error control at α, β nor optimality. When β = 0 (power-one SPRT), this method is conservative and not optimal. The looseness in both cases is caused byovershoot: the test statistic overshoots the thresholds at the stopping time. Numerically calculating thresholds may be infeasible, and most software packages do not do this. We improve the approximate SPRT by modifying the test statistic to avoid overshoot. Our ‘sequential boosting’ techniqueuniformlyimproves power-one SPRTs (β = 0) for simple nulls and alternatives, or for one-sided nulls and alternatives in exponential families. When (β > 0), our techniques provide guaranteed error control at α, β, while needing less samples than the approximate SPRT in our simulations. We also provide several nontrivial extensions: confidence sequences, sampling without replacement and conformal martingales. Lasse Fischer, Aaditya Ramdas |
IEEE Trans. Inf. Theory | 1 |