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Found 19 papers, 12 papers with code
A Scalable Algorithm for Individually Fair K-means Clustering
We present a scalable algorithm for the individually fair ($p$, $k$)-clustering problem introduced by Jung et al. and Mahabadi et al.
Differentially Private Clustering in Data Streams
In this work, we provide the first differentially private streaming algorithms for $k$-means and $k$-median clustering of $d$-dimensional Euclidean data points over a stream with length at most $T$ using $poly(k, d,\log(T))$ space to achieve a constant multiplicative error and a $poly(k, d,\log(T))$ additive error.
Measuring Re-identification Risk
In this work, we present a new theoretical framework to measure re-identification risk in such user representations.
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Then, we exhibit a polynomial-time approximation algorithm with $O(|V|^{2. 5}/ \epsilon)$-additive error, and an exponential-time algorithm that meets the lower bound.
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding.
Smooth Anonymity for Sparse Binary Matrices
When working with user data providing well-defined privacy guarantees is paramount.
Scalable Differentially Private Clustering via Hierarchically Separated Trees
We study the private $k$-median and $k$-means clustering problem in $d$ dimensional Euclidean space.
Smoothly Bounding User Contributions in Differential Privacy
But at the same time, more noise might need to be added to the algorithm in order to keep the algorithm differentially private and this might hurt the algorithm’s performance.
Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions
A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input.
Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions
A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input.
Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.
Sliding Window Algorithms for k-Clustering Problems
The sliding window model of computation captures scenarios in which data is arriving continuously, but only the latest $w$ elements should be used for analysis.
Data Structures and Algorithms
Fair Correlation Clustering
We define a fairlet decomposition with cost similar to the $k$-median cost and this allows us to obtain approximation algorithms for a wide range of fairness constraints.
Clustering without Over-Representation
In this paper we consider clustering problems in which each point is endowed with a color.
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.
Splitter: Learning Node Representations that Capture Multiple Social Contexts
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.
Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters
More precisely, our framework works in two steps: a local ego-net analysis phase, and a global graph partitioning phase .
Ranked #3 on
Community Detection
on Amazon
TRIÈST: Counting Local and Global Triangles in Fully-dynamic Streams with Fixed Memory Size
We present TRI\`EST, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i. e., incident to each vertex) number of triangles in a fully-dynamic graph represented as an adversarial stream of edge insertions and deletions.
Data Structures and Algorithms Databases G.2.2; H.2.8
On a Model for Integrated Information
In this paper we give a thorough presentation of a model proposed by Tononi et al. for modeling \emph{integrated information}, i. e. how much information is generated in a system transitioning from one state to the next one by the causal interaction of its parts and \emph{above and beyond} the information given by the sum of its parts.
