Search Results for author:
Found 41 papers, 10 papers with code
The Cost of Consistency: Submodular Maximization with Constant Recourse
In this work, we study online submodular maximization, and how the requirement of maintaining a stable solution impacts the approximation.
Dynamic Correlation Clustering in Sublinear Update Time
We study the classic problem of correlation clustering in dynamic node streams.
Multi-View Stochastic Block Models
Graph clustering is a central topic in unsupervised learning with a multitude of practical applications.
Consistent Submodular Maximization
Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning.
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.
A quasi-polynomial time algorithm for Multi-Dimensional Scaling via LP hierarchies
Multi-dimensional Scaling (MDS) is a family of methods for embedding an $n$-point metric into low-dimensional Euclidean space.
Multi-Swap $k$-Means++
The $k$-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular $k$-means clustering objective and is known to give an $O(\log k)$-approximation in expectation.
Fully Dynamic Submodular Maximization over Matroids
Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning.
On Classification Thresholds for Graph Attention with Edge Features
In CSBM the nodes and edge features are obtained from a mixture of Gaussians and the edges from a stochastic block model.
Active Learning of Classifiers with Label and Seed Queries
In this work we show that, by carefully combining the two types of queries, a binary classifier can be learned in time $\operatorname{poly}(n+m)$ using only $O(m^2 \log n)$ label queries and $O\big(m \log \frac{m}{\gamma}\big)$ seed queries; the result extends to $k$-class classifiers at the price of a $k! k^2$ multiplicative overhead.
Deletion Robust Non-Monotone Submodular Maximization over Matroids
Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint.
TF-GNN: Graph Neural Networks in TensorFlow
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
Scalable Differentially Private Clustering via Hierarchically Separated Trees
We study the private $k$-median and $k$-means clustering problem in $d$ dimensional Euclidean space.
Near-Optimal Correlation Clustering with Privacy
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more.
Deletion Robust Submodular Maximization over Matroids
Maximizing a monotone submodular function is a fundamental task in machine learning.
Parallel and Efficient Hierarchical k-Median Clustering
In this paper we introduce a new parallel algorithm for the Euclidean hierarchical $k$-median problem that, when using machines with memory $s$ (for $s\in \Omega(\log^2 (n+\Delta+d))$), outputs a hierarchical clustering such that for every fixed value of $k$ the cost of the solution is at most an $O(\min\{d, \log n\} \log \Delta)$ factor larger in expectation than that of an optimal solution.
Efficient and Local Parallel Random Walks
Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning.
Robust Online Correlation Clustering
In correlation clustering we are given a set of points along with recommendations whether each pair of points should be placed in the same cluster or into separate clusters.
Online Facility Location with Multiple Advice
Clustering is a central topic in unsupervised learning and its online formulation has received a lot of attention in recent years.
Correlation Clustering in Constant Many Parallel Rounds
Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining.
On Margin-Based Cluster Recovery with Oracle Queries
We study an active cluster recovery problem where, given a set of $n$ points and an oracle answering queries like "are these two points in the same cluster?
Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries
Previous results show that clusters in Euclidean spaces that are convex and separated with a margin can be reconstructed exactly using only $O(\log n)$ same-cluster queries, where $n$ is the number of input points.
Spectral Clustering Oracles in Sublinear Time
The main technical contribution is a sublinear time oracle that provides dot product access to the spectral embedding of $G$ by estimating distributions of short random walks from vertices in $G$.
Data Structures and Algorithms
Fast and Accurate $k$-means++ via Rejection Sampling
$k$-means++ \cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance.
Online MAP Inference of Determinantal Point Processes
In this paper, we provide an efficient approximation algorithm for finding the most likelihood configuration (MAP) of size $k$ for Determinantal Point Processes (DPP) in the online setting where the data points arrive in an arbitrary order and the algorithm cannot discard the selected elements from its local memory.
On Mean Estimation for Heteroscedastic Random Variables
We study the problem of estimating the common mean $\mu$ of $n$ independent symmetric random variables with different and unknown standard deviations $\sigma_1 \le \sigma_2 \le \cdots \le\sigma_n$.
InstantEmbedding: Efficient Local Node Representations
In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations.
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
Exact Recovery of Mangled Clusters with Same-Cluster Queries
Given a finite set of input points, and an oracle revealing whether any two points lie in the same cluster, our goal is to recover all clusters exactly using as few queries as possible.
Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
We show how one can achieve the tight $(1/2)$-approximation guarantee with $O(k)$ shared memory while minimizing not only the required rounds of computations but also the total number of communicated bits.
Mallows Models for Top-k Lists
The classic Mallows model is a widely-used tool to realize distributions on per- mutations.
Parallel and Streaming Algorithms for K-Core Decomposition
The $k$-core decomposition is a fundamental primitive in many machine learning and data mining applications.
Fair Clustering Through Fairlets
We show that any fair clustering problem can be decomposed into first finding good fairlets, and then using existing machinery for traditional clustering algorithms.
Affinity Clustering: Hierarchical Clustering at Scale
In particular, we propose affinity, a novel hierarchical clustering based on Boruvka's MST algorithm.
One-Shot Coresets: The Case of k-Clustering
Scaling clustering algorithms to massive data sets is a challenging task.
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
Algorithms for $\ell_p$ Low-Rank Approximation
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise $\ell_p$-approximation error, for any $p \geq 1$; the case $p = 2$ is the classical SVD problem.
Consistent k-Clustering
The study of online algorithms and competitive analysis provides a solid foundation for studying the quality of irrevocable decision making when the data arrives in an online manner.
Community Detection on Evolving Graphs
In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph.
Distributed Balanced Clustering via Mapping Coresets
Large-scale clustering of data points in metric spaces is an important problem in mining big data sets.
Local Graph Clustering Beyond Cheeger's Inequality
We also prove that our analysis is tight, and perform empirical evaluation to support our theory on both synthetic and real data.
