no code implementations • 1 Feb 2024 • Jin Sima, Changlong Wu, Olgica Milenkovic, Wojciech Szpankowski
We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy.
1 code implementation • 16 Dec 2023 • Vishal Rana, Jianhao Peng, Chao Pan, Hanbaek Lyu, Albert Cheng, Minji Kim, Olgica Milenkovic
First, we demonstrate that online cvxNDL retains the accuracy of classical DL methods while simultaneously ensuring unique interpretability and scalability.
2 code implementations • 14 Aug 2023 • Saurav Prakash, Jin Sima, Chao Pan, Eli Chien, Olgica Milenkovic
Third, we compute the complexity of the convex hulls in hyperbolic spaces to assess the extent of data leakage; at the same time, in order to limit communication cost for the hulls, we propose a new quantization method for the Poincar\'e disc coupled with Reed-Solomon-like encoding.
no code implementations • 31 Jul 2023 • Ananthan Nambiar, Chao Pan, Vishal Rana, Mahdi Cheraghchi, João Ribeiro, Sergei Maslov, Olgica Milenkovic
Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide.
1 code implementation • 21 May 2023 • Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu
Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances.
1 code implementation • 6 Nov 2022 • Chao Pan, Eli Chien, Olgica Milenkovic
As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems.
1 code implementation • 28 Oct 2022 • Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, Olgica Milenkovic
We introduce, for the first time, the problem of machine unlearning for FC, and propose an efficient unlearning mechanism for a customized secure FC framework.
1 code implementation • 18 Jun 2022 • Eli Chien, Chao Pan, Olgica Milenkovic
For example, when unlearning $20\%$ of the nodes on the Cora dataset, our approach suffers only a $0. 1\%$ loss in test accuracy while offering a $4$-fold speed-up compared to complete retraining.
1 code implementation • 19 May 2022 • Eli Chien, Puoya Tabaghi, Olgica Milenkovic
Furthermore, it is currently not known how to choose the most suitable approximation objective for noisy fitting.
1 code implementation • 7 Mar 2022 • Chao Pan, Eli Chien, Puoya Tabaghi, Jianhao Peng, Olgica Milenkovic
The excellent performance of the Poincar\'e second-order and strategic perceptrons shows that the proposed framework can be extended to general machine learning problems in hyperbolic spaces.
4 code implementations • ICLR 2022 • Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S Dhillon
We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
Ranked #2 on Node Property Prediction on ogbn-papers100M
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.
1 code implementation • ICLR 2022 • 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.
1 code implementation • 19 Feb 2021 • Puoya Tabaghi, Chao Pan, Eli Chien, Jianhao Peng, Olgica Milenkovic
The results show that classification in low-dimensional product space forms for scRNA-seq data offers, on average, a performance improvement of $\sim15\%$ when compared to that in Euclidean spaces of the same dimension.
no code implementations • 18 Jan 2021 • Xujun Liu, Olgica Milenkovic, George V. Moustakides
We study the secretary problem in which rank-ordered lists are generated by the Mallows model and the goal is to identify the highest-ranked candidate through a sequential interview process which does not allow rejected candidates to be revisited.
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 • 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 2018 • Pan Li, Olgica Milenkovic
We introduce a new approach to decomposable submodular function minimization (DSFM) that exploits incidence relations.
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.