7 papers
Fast Construction of Learned Count-Min Sketch via Ternary Search
Ryusuke Inami, Yusuke Matsui
The Learned Count-Min Sketch (LCMS) is a learned data structure that estimates element frequencies in a multiset and has been experimentally shown to outperform classical data stru…
PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
Haruki Yajima, Yusuke Matsui
Tree ensembles are machine learning models with strong predictive performance and interpretability, and remain widely used for tabular data. Standard pruning methods for tree ensem…
Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions
Atsuki Sato, Martin Aumüller, Yusuke Matsui
Learned indexes are a class of index data structures that enable fast search by approximating the cumulative distribution function (CDF) using machine learning models (Kraska et al…
Optimized Learned Count-Min Sketch
Kyosuke Nishishita, Atsuki Sato, Yusuke Matsui
Count-Min Sketch (CMS) is a memory-efficient data structure for estimating the frequency of elements in a multiset. Learned Count-Min Sketch (LCMS) enhances CMS with a machine lear…
PCF Learned Sort: a Learning Augmented Sort Algorithm with Expected Complexity
Atsuki Sato, Yusuke Matsui
Sorting is one of the most fundamental algorithms in computer science. Recently, Learned Sorts, which use machine learning to improve sorting speed, have attracted attention. While…
Cascaded Learned Bloom Filter for Optimizing Model-Filter Size Balance and Fast Rejection
Atsuki Sato, Yusuke Matsui
Recent studies have demonstrated that learned Bloom filters (LBFs), which combine machine learning with the classical Bloom filter, can achieve superior memory efficiency. However,…