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1 code implementation • 8 Sep 2021 • Eli Chien, Chao Pan, Puoya Tabaghi, Olgica Milenkovic

For hierarchical data, the space of choice is a hyperbolic space since it guarantees low-distortion embeddings for tree-like structures.

no code implementations • 24 Jun 2021 • Eli Chien, Chao Pan, Jianhao Peng, Olgica Milenkovic

We propose AllSet, a new hypergraph neural network paradigm that represents a highly general framework for (hyper)graph neural networks and for the first time implements hypergraph neural network layers as compositions of two multiset functions that can be efficiently learned for each task and each dataset.

no code implementations • 18 Jan 2021 • Xujun Liu, Olgica Milenkovic, George V. Moustakides

If the Genie confirms the query, the selection process terminates.

Methodology Discrete Mathematics Information Theory Combinatorics Information Theory 05A

no code implementations • 10 Nov 2020 • Ryan Gabrys, Srilakshmi Pattabiraman, Vishal Rana, João Ribeiro, Mahdi Cheraghchi, Venkatesan Guruswami, Olgica Milenkovic

The first part of the paper presents a review of the gold-standard testing protocol for Covid-19, real-time, reverse transcriptase PCR, and its properties and associated measurement data such as amplification curves that can guide the development of appropriate and accurate adaptive group testing protocols.

no code implementations • 17 Jun 2020 • Puoya Tabaghi, Jianhao Peng, Olgica Milenkovic, Ivan Dokmanić

To study this question, we introduce the notions of the \textit{ordinal capacity} of a target space form and \emph{ordinal spread} of the similarity measurements.

1 code implementation • ICLR 2021 • Eli Chien, Jianhao Peng, Pan Li, Olgica Milenkovic

We address these issues by introducing a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights so as to jointly optimize node feature and topological information extraction, regardless of the extent to which the node labels are homophilic or heterophilic.

no code implementations • 14 Jun 2020 • Eli Chien, Olgica Milenkovic, Angelia Nedich

Here we introduce the first known approach to support estimation in the presence of sampling artifacts and errors where each sample is assumed to arise from a Poisson repeat channel which simultaneously captures repetitions and deletions of samples.

no code implementations • 8 Nov 2019 • Anuththari Gamage, Eli Chien, Jianhao Peng, Olgica Milenkovic

Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity patterns known as network motifs.

no code implementations • 22 Oct 2019 • Chao Pan, S. M. Hossein Tabatabaei Yazdi, S Kasra Tabatabaei, Alvaro G. Hernandez, Charles Schroeder, Olgica Milenkovic

The main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis.

no code implementations • 20 Oct 2019 • Eli Chien, Pan Li, Olgica Milenkovic

We describe the first known mean-field study of landing probabilities for random walks on hypergraphs.

no code implementations • NeurIPS 2019 • Pan Li, Eli Chien, Olgica Milenkovic

Landing probabilities (LP) of random walks (RW) over graphs encode rich information regarding graph topology.

1 code implementation • NeurIPS 2019 • Abhishek Agarwal, Jianhao Peng, Olgica Milenkovic

We address both problems by proposing the first online convex MF algorithm that maintains a collection of constant-size sets of representative data samples needed for interpreting each of the basis (Ding et al. [2010]) and has the same almost sure convergence guarantees as the online learning algorithm of Mairal et al. [2010].

no code implementations • 26 Feb 2019 • Pan Li, Niao He, Olgica Milenkovic

We introduce a new convex optimization problem, termed quadratic decomposable submodular function minimization (QDSFM), which allows to model a number of learning tasks on graphs and hypergraphs.

no code implementations • 22 Jan 2019 • i, Chien, Olgica Milenkovic

We introduce a new method for estimating the support size of an unknown distribution which provably matches the performance bounds of the state-of-the-art techniques in the area and outperforms them in practice.

no code implementations • 16 Dec 2018 • Subhadeep Paul, Olgica Milenkovic, Yuguo Chen

In particular, we prove non-asymptotic upper bounds on the misclustering error of spectral community detection for a SupSBM setting in which triangles or 3-uniform hyperedges are superimposed with undirected edges.

no code implementations • 5 Nov 2018 • Pan Li, Gregory J. Puleo, Olgica Milenkovic

Our contributions are as follows: We first introduce several variants of motif correlation clustering and then show that these clustering problems are NP-hard.

1 code implementation • NeurIPS 2018 • Pan Li, Niao He, Olgica Milenkovic

The problem is closely related to decomposable submodular function minimization and arises in many learning on graphs and hypergraphs settings, such as graph-based semi-supervised learning and PageRank.

no code implementations • NeurIPS 2018 • I Chien, Chao Pan, Olgica Milenkovic

We consider the problem of approximate $K$-means clustering with outliers and side information provided by same-cluster queries and possibly noisy answers.

1 code implementation • NeurIPS 2018 • Pan Li, Olgica Milenkovic

We introduce a new approach to decomposable submodular function minimization (DSFM) that exploits incidence relations.

1 code implementation • ICML 2018 • Pan Li, Olgica Milenkovic

We introduce submodular hypergraphs, a family of hypergraphs that have different submodular weights associated with different cuts of hyperedges.

1 code implementation • NeurIPS 2017 • Pan Li, Olgica Milenkovic

Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics.

no code implementations • 28 Jan 2017 • Pan Li, Arya Mazumdar, Olgica Milenkovic

We propose a novel rank aggregation method based on converting permutations into their corresponding Lehmer codes or other subdiagonal images.

no code implementations • 28 Jan 2017 • Pan Li, Olgica Milenkovic

We introduce a new family of minmax rank aggregation problems under two distance measures, the Kendall {\tau} and the Spearman footrule.

no code implementations • 25 Jan 2016 • Jack P. Hou, Amin Emad, Gregory J. Puleo, Jian Ma, Olgica Milenkovic

To test $C^3$, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying driver genes.

no code implementations • 26 Jun 2015 • Gregory J. Puleo, Olgica Milenkovic

We consider a generalized version of the correlation clustering problem, defined as follows.

no code implementations • 3 Nov 2014 • Gregory J. Puleo, Olgica Milenkovic

We consider the problem of correlation clustering on graphs with constraints on both the cluster sizes and the positive and negative weights of edges.

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