Lasse Fischer

dblp:430/6729 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Coding theory
error-correcting codes
1.012026
Improving Wald's (Approximate) Sequential Probability Ratio Test by Avoiding Overshoot · IEEE Trans. Inf. Theory 2026
Information theory
hypothesis testing
1.012026
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
1.012026
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
YearPublicationVenuePosition
2026 Improving Wald's (Approximate) Sequential Probability Ratio Test by Avoiding Overshoot
abstract
Wald’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. Theory1