18 citations · 94 across the 21 of their papers we have counts for
21 papers
Graph Matching in Correlated Stochastic Block Models for Improved Graph Clustering
Joonhyuk Yang, Hye Won Chung
We consider community detection from multiple correlated graphs sharing the same community structure. The correlated graphs are generated by independent subsampling of a parent gra…
Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant Correlation
Joonhyuk Yang, Dongpil Shin, Hye Won Chung
We consider the problem of graph matching, or learning vertex correspondence, between two correlated stochastic block models (SBMs). The graph matching problem arises in various fi…
Detection problems in the spiked matrix models
Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
We study the statistical decision process of detecting the low-rank signal from various signal-plus-noise type data matrices, known as the spiked random matrix models. We first sho…
Data Valuation Without Training of a Model
Nohyun Ki, Hoyong Choi, Hye Won Chung
Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model. Such attempts reveal c…
Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing
Hyeonsu Jeong, Hye Won Chung
Crowdsourcing has emerged as an effective platform for labeling large amounts of data in a cost- and time-efficient manner. Most previous work has focused on designing an efficient…
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization
Daesung Kim, Hye Won Chung
The nonconvex formulation of the matrix completion problem has received significant attention in recent years due to its affordable complexity compared to the convex formulation. G…
Asymptotic Normality of Log Likelihood Ratio and Fundamental Limit of the Weak Detection for Spiked Wigner Matrices
Hye Won Chung, Jiho Lee, Ji Oon Lee
We consider the problem of detecting the presence of a signal in a rank-one spiked Wigner model. For general non-Gaussian noise, assuming that the signal is drawn from the Rademach…
A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits
Doyeon Kim, Jeonghwan Lee, Hye Won Chung
Crowdsourcing system has emerged as an effective platform for labeling data with relatively low cost by using non-expert workers. Inferring correct labels from multiple noisy answe…
Detection of Signal in the Spiked Rectangular Models
Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
We consider the problem of detecting signals in the rank-one signal-plus-noise data matrix models that generalize the spiked Wishart matrices. We show that the principal component…
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks
Jinhee Lee, Haeri Kim, Youngkyu Hong +1
Despite remarkable performance in producing realistic samples, Generative Adversarial Networks (GANs) often produce low-quality samples near low-density regions of the data manifol…
Crowdsourced Labeling for Worker-Task Specialization Model
Doyeon Kim, Hye Won Chung
We consider crowdsourced labeling under a -type worker-task specialization model, where each worker and task is associated with one particular type among a finite set of types a…
Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE
Jeonghwan Lee, Daesung Kim, Hye Won Chung
We study hypergraph clustering in the weighted -uniform hypergraph stochastic block model (\textsf{-WHSBM}), where each edge consisting of nodes from the same community h…
Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm
Daesung Kim, Hye Won Chung
We consider a query-based data acquisition problem for binary classification of unknown labels, which has diverse applications in communications, crowdsourcing, recommender systems…
Weak Detection in the Spiked Wigner Model with General Rank
Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
We study the statistical decision process of detecting the signal from a `signal+noise' type matrix model with an additive Wigner noise. We propose a hypothesis test based on the l…
Shallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution
Youngjae Min, Hye Won Chung
Guiding the design of neural networks is of great importance to save enormous resources consumed on empirical decisions of architectural parameters. This paper constructs shallow s…
Weak detection in the spiked Wigner model
Hye Won Chung, Ji Oon Lee
We consider the weak detection problem in a rank-one spiked Wigner data matrix where the signal-to-noise ratio is small so that reliable detection is impossible. We propose a hypot…
Parity Queries for Binary Classification
Hye Won Chung, Ji Oon Lee, Doyeon Kim +1
Consider a query-based data acquisition problem that aims to recover the values of binary variables from parity (XOR) measurements of chosen subsets of the variables. Assume th…
Fundamental Limits on Data Acquisition: Trade-offs between Sample Complexity and Query Difficulty
Hye Won Chung, Ji Oon Lee, Alfred O. Hero
We consider query-based data acquisition and the corresponding information recovery problem, where the goal is to recover binary variables (information bits) from parity measur…
On capacity of optical communications over a lossy bosonic channel with a receiver employing the most general coherent electro-optic feedback control
Hye Won Chung, Saikat Guha, Lizhong Zheng
We study the problem of designing optical receivers to discriminate between multiple coherent states using coherent processing receivers---i.e., one that uses arbitrary coherent fe…
Unequal Error Protection Querying Policies for the Noisy 20 Questions Problem
Hye Won Chung, Brian M. Sadler, Lizhong Zheng +1
In this paper, we propose an open-loop unequal-error-protection querying policy based on superposition coding for the noisy 20 questions problem. In this problem, a player wishes t…
Bounds on Variance for Unimodal Distributions
Hye Won Chung, Brian M. Sadler, Alfred O. Hero
We show a direct relationship between the variance and the differential entropy for subclasses of symmetric and asymmetric unimodal distributions by providing an upper bound on var…