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Found 9 papers, 0 papers with code
Hierarchical Clustering: Objective Functions and Algorithms
For similarity-based hierarchical clustering, Dasgupta showed that the divisive sparsest-cut approach achieves an $O(\log^{3/2} n)$-approximation.
Instance-Optimality in the Noisy Value-and Comparison-Model --- Accept, Accept, Strong Accept: Which Papers get in?
In this paper, we show optimal worst-case query complexity for the \textsc{max},\textsc{threshold-$v$} and \textsc{Top}-$k$ problems.
Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms
In this work, we take a different approach, based on the observation that the consistency axiom fails to be satisfied when the “correct” number of clusters changes.
Hierarchical Clustering Beyond the Worst-Case
Hiererachical clustering, that is computing a recursive partitioning of a dataset to obtain clusters at increasingly finer granularity is a fundamental problem in data analysis.
Learning Hierarchically Structured Concepts
Our main goal is to introduce a general framework for these tasks and prove formally how both (recognition and learning) can be achieved.
Online Page Migration with ML Advice
In contrast, we show that if the algorithm is given a prediction of the input sequence, then it can achieve a competitive ratio that tends to $1$ as the prediction error rate tends to $0$.
On the Power of Louvain in the Stochastic Block Model
A classic problem in machine learning and data analysis is to partition the vertices of a network in such a way that vertices in the same set are densely connected and vertices in different sets are loosely connected.
Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy
Among the various aspects of algorithmic fairness studied in recent years, the tension between satisfying both \textit{sufficiency} and \textit{separation} -- e. g. the ratios of positive or negative predictive values, and false positive or false negative rates across groups -- has received much attention.
Learning Hierarchically-Structured Concepts II: Overlapping Concepts, and Networks With Feedback
We continue our study from Lynch and Mallmann-Trenn (Neural Networks, 2021), of how concepts that have hierarchical structure might be represented in brain-like neural networks, how these representations might be used to recognize the concepts, and how these representations might be learned.
