no code implementations • 18 Oct 2024 • Emanuele Natale, Davide Ferre', Giordano Giambartolomei, Frédéric Giroire, Frederik Mallmann-Trenn

Central to our results, is the proof of an essentially tight bound on the Random Fixed-Size Subset Sum Problem (RFSS), a variant of the RSS Problem in which we only ask for subsets of a given size, which is of independent interest.

no code implementations • 19 Apr 2023 • Nancy Lynch, Frederik Mallmann-Trenn

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.

no code implementations • 24 May 2022 • Limor Gultchin, Vincent Cohen-Addad, Sophie Giffard-Roisin, Varun Kanade, Frederik Mallmann-Trenn

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.

no code implementations • NeurIPS 2020 • Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic

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.

no code implementations • 9 Jun 2020 • Piotr Indyk, Frederik Mallmann-Trenn, Slobodan Mitrović, Ronitt Rubinfeld

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 $0$.

no code implementations • 10 Sep 2019 • Nancy Lynch, Frederik Mallmann-Trenn

Our main goal is to introduce a general framework for these tasks and prove formally how both (recognition and learning) can be achieved.

no code implementations • NeurIPS 2018 • Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn

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.

no code implementations • 21 Jun 2018 • Vincent Cohen-Addad, Frederik Mallmann-Trenn, Claire Mathieu

In this paper, we show optimal worst-case query complexity for the \textsc{max},\textsc{threshold-$v$} and \textsc{Top}-$k$ problems.

no code implementations • NeurIPS 2017 • Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn

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.

no code implementations • 7 Apr 2017 • Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu

For similarity-based hierarchical clustering, Dasgupta showed that the divisive sparsest-cut approach achieves an $O(\log^{3/2} n)$-approximation.

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