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Found 8 papers, 0 papers with code
On a Near-Optimal \& Efficient Algorithm for the Sparse Pooled Data Problem
In this article, we resolve the question whether this $\log n$-gap is artificial or of a fundamental nature by the design of an efficient algorithm, called \algoname, based upon a novel pooling scheme on a number of pools very close to the information-theoretic threshold.
An unsupervised learning approach to evaluate questionnaire data -- what one can learn from violations of measurement invariance
In several branches of the social sciences and humanities, surveys based on standardized questionnaires are a prominent research tool.
A Notion of Feature Importance by Decorrelation and Detection of Trends by Random Forest Regression
More specifically, we are interested in the strength of the influence -- i. e., is the feature relevant?
Efficient Approximate Recovery from Pooled Data Using Doubly Regular Pooling Schemes
In the pooled data problem we are given $n$ agents with hidden state bits, either $0$ or $1$.
Statistical and Computational Phase Transitions in Group Testing
For the Bernoulli design, we determine the precise number of tests required to solve the associated detection problem (where the goal is to distinguish between a group testing instance and pure noise), improving both the upper and lower bounds of Truong, Aldridge, and Scarlett (2020).
Inference of a Rumor's Source in the Independent Cascade Model
In this work we present a maximum likelihood estimator for the rumor's source, given a snapshot of the process in terms of a set of active nodes $X$ after $t$ steps.
Distributed Reconstruction of Noisy Pooled Data
In the pooled data problem we are given a set of $n$ agents, each of which holds a hidden state bit, either $0$ or $1$.
Efficient and accurate group testing via Belief Propagation: an empirical study
The group testing problem asks for efficient pooling schemes and algorithms that allow to screen moderately large numbers of samples for rare infections.
