# A Rademacher Complexity Based Method for Controlling Power and Confidence Level in Adaptive Statistical Analysis

@article{Stefani2019ARC, title={A Rademacher Complexity Based Method for Controlling Power and Confidence Level in Adaptive Statistical Analysis}, author={Lorenzo De Stefani and Eli Upfal}, journal={2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, year={2019}, pages={71-80} }

While standard statistical inference techniques and machine learning generalization bounds assume that tests are run on data selected independently of the hypotheses, practical data analysis and machine learning are usually iterative and adaptive processes where the same holdout data is often used for testing a sequence of hypotheses (or models), which may each depend on the outcome of the previous tests on the same data. In this work, we present RADABOUND a rigorous, efficient and practical…

## 5 Citations

### MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

- Computer ScienceKDD
- 2020

MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself, and TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power than existing methods offering the same guarantees.

### MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

- Computer ScienceACM Trans. Knowl. Discov. Data
- 2022

MCRapper and TFP-R outperform the state-of-the-art for their respective tasks and give guarantees on the probability of including any false positives (precision) and exhibits higher statistical power than existing methods offering the same guarantees.

### Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages

- Computer Science, MathematicsACM Transactions on Knowledge Discovery from Data
- 2022

Bavarian, a collection of sampling-based algorithms for approximating the Betweenness Centrality of all vertices in a graph, is presented and it is proved that, for all estimators, the sample size sufficient to achieve a desired approximation guarantee depends on the vertex-diameter of the graph, an easy-to-bound characteristic quantity.

### SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds

- Computer ScienceArXiv
- 2021

SILVAN relies on a novel estimation scheme providing non-uniform bounds on the deviation of the estimates of the betweenness centrality of all the nodes from their true values, and a refined characterisation of the number of samples required to obtain a high-quality approximation.

### Sharper convergence bounds of Monte Carlo Rademacher Averages through Self-Bounding functions

- Computer Science, MathematicsArXiv
- 2020

This work derives sharper probabilistic concentration bounds for the Monte Carlo Empirical Rademacher Averages (MCERA), which are proved through recent results on the concentration of self-bounding functions, and derives novel variance-aware bounds to the supremum deviations.

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