Kernel-based Cumulative Sum (KCUSUM) Algorithm for Real-time Adaptive Sampling Change Point Detection
The Kernel-based Cumulative Sum (KCUSUM) algorithm is a non-parametric extension of the traditional Cumulative Sum (CUSUM) method, which can effectively detect changes in real-time data streams without requiring prior knowledge of the underlying data distribution.
Improved Approximation Algorithm for Covering Pliable Set Families
The paper presents an improved approximation algorithm for the Set Family Edge Cover problem with pliable set families, achieving an approximation ratio of 10, which improves upon the previous ratio of 16.
An Efficient O(n log n) Algorithm for Sorting Signed Permutations by Reversals
This article presents the first algorithm that runs in O(n log n) time in the worst case for the Signed Sorting by Reversals problem, which transforms a signed permutation into the identity permutation using a minimum-length sequence of reversals.
An Efficient Algorithm for Finding Equal Subset Sums in the Pigeonhole Setting
Given n positive integers with total sum less than 2^n-1, the algorithm efficiently finds two distinct subsets with equal subset sums.
Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling: Analysis and Insights
Prior diffusion in Langevin algorithms enables dimension-independent convergence for non-log-concave distributions.
Interval-Constrained Bipartite Matching over Time: Analysis and Algorithms
FirstFit and Online-EDF algorithms analyzed for Interval-Constrained Bipartite Matching.
Max-Cut with ε-Accurate Predictions: Improving Approximation Ratios
Predictions can be leveraged to improve Max-Cut approximation ratios, with ε-accurate predictions enhancing algorithm performance.
Efficient Algorithms for Electric Car Travel Plans with Charging Stations
Efficient algorithms reduce computing plans between junctions to two problems: optimal energetic paths and standard shortest paths.
Single-Sample Prophet Inequalities via Greedy-Ordered Selection: Algorithm and Analysis
Developing a versatile greedy-based technique for Single-Sample Prophet Inequalities directly, improving competitive guarantees.
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
Greedy algorithms outperform UCB in many-armed bandit problems.
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