22 citations · 66 across the 12 of their papers we have counts for
12 papers
Minimum-Risk Recalibration of Classifiers
Zeyu Sun, Dogyoon Song, Alfred Hero
Recalibrating probabilistic classifiers is vital for enhancing the reliability and accuracy of predictive models. Despite the development of numerous recalibration algorithms, ther…
Errors-in-variables Fréchet Regression with Low-rank Covariate Approximation
Kyunghee Han, Dogyoon Song
Fréchet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables. However, its practical applicability has been hindered by…
Robustness-preserving Lifelong Learning via Dataset Condensation
Jinghan Jia, Yihua Zhang, Dogyoon Song +2
Lifelong learning (LL) aims to improve a predictive model as the data source evolves continuously. Most work in this learning paradigm has focused on resolving the problem of 'cata…
On Separability of Covariance in Multiway Data Analysis
Dogyoon Song, Alfred O. Hero
Multiway data analysis aims to uncover patterns in data structured as multi-indexed arrays, with multiway covariance playing a crucial role in many applications. However, the high…
Kronecker-structured Covariance Models for Multiway Data
Yu Wang, Zeyu Sun, Dogyoon Song +1
Many applications produce multiway data of exceedingly high dimension. Modeling such multi-way data is important in multichannel signal and video processing where sensors produce m…
On Approximations of the PSD Cone by a Polynomial Number of Smaller-sized PSD Cones
Dogyoon Song, Pablo A. Parrilo
We study the problem of approximating the cone of positive semidefinite (PSD) matrices with a cone that can be described by smaller-sized PSD constraints. Specifically, we ask the…
Local Minima Structures in Gaussian Mixture Models
Yudong Chen, Dogyoon Song, Xumei Xi +1
We investigate the landscape of the negative log-likelihood function of Gaussian Mixture Models (GMMs) with a general number of components in the population limit. As the objective…
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
Devavrat Shah, Dogyoon Song, Zhi Xu +1
We consider the question of learning -function in a sample efficient manner for reinforcement learning with continuous state and action spaces under a generative model. If -f…
On Robustness of Principal Component Regression
Anish Agarwal, Devavrat Shah, Dennis Shen +1
Principal component regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structur…
Learning RUMs: Reducing Mixture to Single Component via PCA
Devavrat Shah, Dogyoon Song
We consider the problem of learning a mixture of Random Utility Models (RUMs). Despite the success of RUMs in various domains and the versatility of mixture RUMs to capture the het…
Deconvolution with Unknown Error Distribution Interpreted as Blind Isotonic Regression
Devavrat Shah, Dogyoon Song
Deconvolution is a statistical inverse problem to estimate the distribution of a random variable based on its noisy observations. Despite the extensive studies on the topic, deconv…
Nearest Neighbors for Matrix Estimation Interpreted as Blind Regression for Latent Variable Model
Yihua Li, Devavrat Shah, Dogyoon Song +1
We consider the setup of nonparametric {\em blind regression} for estimating the entries of a large matrix, when provided with a small, random fraction of noisy measur…