Adaptive Threshold Sampling

TL;DR

This paper introduces a flexible framework for adaptive threshold sampling that dynamically adjusts sampling probabilities, simplifying implementation and enabling new sampling strategies for various complex data analysis tasks.

Contribution

It presents a novel framework for adaptive threshold sampling that handles dependence among samples and applies to diverse problems like top-K and stratified sampling.

Findings

Framework simplifies adaptive sampling implementation.

New algorithms for top-K and stratified sampling.

Addresses dependence issues in adaptive sampling.

Abstract

Sampling is a fundamental problem in computer science and statistics. However, for a given task and stream, it is often not possible to choose good sampling probabilities in advance. We derive a general framework for adaptively changing the sampling probabilities via a collection of thresholds.In general, adaptive sampling procedures introduce dependence amongst the sampled points, making it difficult to compute expectations and ensure estimators are unbiased or consistent. Our framework address this issue and further shows when adaptive thresholds can be treated as if they were fixed thresholds which samples items independently. This makes our adaptive sampling schemes simple to apply as there is no need to create custom estimators for the sampling method. Using our framework, we derive new samplers that can address a broad range of new and existing problems including sampling with memory rather than sample size budgets, stratified samples, multiple objectives, distinct counting, and sliding windows. In particular, we design a sampling procedure for the top-K problem where, unlike in the heavy-hitter problem, the sketch size and sampling probabilities are adaptively chosen.