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  1. Free, publicly-accessible full text available May 7, 2025
  2. Proceedings of the 40th International Conference on Machine Learning, PMLR 202:37919-37951, 2023. 
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    Free, publicly-accessible full text available October 27, 2024
  3. Approximating quantiles and distributions over streaming data has been studied for roughly two decades now. Recently, Karnin, Lang, and Liberty proposed the first asymptotically optimal algorithm for doing so. This manuscript complements their theoretical result by providing a practical variants of their algorithm with improved constants. For a given sketch size, our techniques provably reduce the upper bound on the sketch error by a factor of two. These improvements are verified experimentally. Our modified quantile sketch improves the latency as well by reducing the worst-case update time from O(1ε) down to O(log1ε). 
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  4. Packet drops caused by congestion are a fundamental problem in network operation. Yet, it is difficult to detect where drops are happening, let alone which flows are most affected. Detecting the small-timescale drops caused by short bursts of traffic is even more challenging, and traditional monitoring techniques can easily miss them. To uncover packet drops as they occur inside a switch, the analysis must be real-time, fine-grained, and efficient. However, modern switches have distributed packet-processing pipelines that see either the arriving or departing traffic, but not the packet drops. Plus, they do not have enough memory to store per-flow state. Our MIDST system addresses these challenges through a distributed compact data structure with lightweight coordination between ingress and egress pipelines. MIDST identifies the flows experiencing loss, as well as the bursty flows responsible, across different burst durations. Our evaluation with real-world traces and TCP connections shows that MIDST uses little memory (e.g., 320KB) while providing high accuracy (95% to 98%) under varying loss rates and burst durations. We evaluate a low-rate DDoS attack and demonstrate the potential use of our measurement results for attack detection and mitigation. 
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  5. Summary

    A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the “scale-agnostic” $G_{+}$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $k$ groups, we show that $G_{+}$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $G_{+}$, referred to as $H_{+}$, and demonstrate that $H_{+}$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $H_{+}$, which are available in the $\mathtt{fasthplus}$ R package.

     
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