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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Guarded Epoch Bloom Filters for Sliding-Window Membership
[Submitted on 17 Jun 2026] · 2026-06-18 · via cs.DS updates on arXiv.org

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Abstract:Approximate membership queries in streams often need recent-window semantics rather than membership over all items ever seen. This paper studies guarded epoch Bloom filters, a sliding-window alternative to counting and stable Bloom filters. The structure partitions a fixed bit budget into rotating epochs, inserts only into the current epoch, clears whole segments at epoch boundaries, and keeps one additional guard epoch. This guard yields a deterministic live-window invariant: every item inserted in the last W positions remains represented, while rotation-induced stale retention is bounded by one epoch beyond the target window. We give the construction, prove its live-coverage and bounded-staleness properties, derive a false-positive approximation, and include a blocked variant that improves locality by confining probes to one block per epoch. Experiments cover 225 synthetic streaming configurations and 45 configurations from a timestamp-ordered web-server access-log stream. At 14 bits per live item, the guarded epoch filter reduces median synthetic false positives from 0.191 for a four-bit counting Bloom baseline to 0.02225 while preserving zero measured live-key false negatives. The method is not a replacement for exact deletion; it targets systems where bounded stale positives are acceptable but false negatives inside the live window are not.

Submission history

From: Levent Sarioglu [view email]
[v1] Wed, 17 Jun 2026 15:45:59 UTC (2,249 KB)