Yuekai Sun

Papers

Please see my Google Scholar profile for a complete list of publications and citation metrics.

Preprints

SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
M Yurochkin, Y Sun.

Minimax optimal approaches to the label shift problem
S Maity, Y Sun, M Banerjee.

Communication-efficient integrative regression in high dimensions
S Maity, Y Sun, M Banerjee.

On conditional parity as a notion of non-discrimination in machine learning
Y Ritov, Y Sun, R Zhao.

Journal papers

Uniform bounds for invariant subspace perturbations
A Damle, Y Sun. SIAM Journal of Matrix Analysis and Applications (in press).
/asdamle/rowwise-perturbation

Statistical convergence of the EM algorithm on Gaussian mixture models
R Zhao, Y Li, Y Sun. Electronic Journal of Statistics (2020).

A geometric approach to archetypal analysis and nonnegative matrix factorization
A Damle, Y Sun. Technometrics (2017).
2017 Technometrics Prize

Communication-efficient sparse regression
JD Lee, Q Liu, Y Sun, JE Taylor. Journal of Machine Learning Research (2017).

Exact post-selection inference with the lasso
JD Lee, DL Sun, Y Sun, JE Taylor. Annals of Statistics (2016).
/selective-inference

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data
L Yang, J Tan, EJ O’Brien, … Y Sun, MA Saunders, BO Palsson. Proceedings of the National Academy of Sciences (2015).

On model selection consistency of regularized M-estimators
JD Lee, Y Sun, JE Taylor. Electronic Journal of Statistics (2015).
A short version appeared at NIPS 2013.

Proximal Newton-type methods for minimizing composite functions
JD Lee, Y Sun, MA Saunders. SIAM Journal on Optimization (2014).
A short version appeared at NIPS 2012.
/yuekai/PNOPT

Robust flux balance analysis of multiscale biochemical reaction networks
Y Sun, RMT Fleming, I Thiele, MA Saunders. BMC Bioinformatics (2013).
/opencobra/cobratoolbox

Conference papers

Two simple ways to learn individual fairness metrics from data
D Mukherjee, M Yurochkin, M Banerjee, Y Sun. ICML 2020.
/mdebumich/Fair_metric_learning

Auditing ML models for individual bias and unfairness
S Xue, M Yurochkin, Y Sun. AISTATS 2020.

Federated Learning with Matched Averaging
H Wang, M Yurochkin, Y Sun, D Papailiopoulos, Y Khazaeni. ICLR 2020.
/IBM/FedMA

Training individually fair machine learning models with Sensitive Subspace Robustness
M Yurochkin, A Bower, Y Sun. ICLR 2020.
/IBM/sensitive-subspace-robustness

Dirichlet Simplex Nest and Geometric Inference
M Yurochkin, A Guha, Y Sun, XL Nguyen. ICML 2019.
/moonfolk/VLAD

Precision Matrix Estimation with Noisy and Missing Data
R Fan, B Jang, Y Sun, S Zhou. AISTATS 2019.

Debiasing representations by removing unwanted variation due to protected attributes
A Bower, L Niss, Y Sun, A Vargo. FAT/ML 2018.
/Amandarg/debias

Feature-distributed sparse regression: a screen-and-clean approach
J Yang, MW Mahoney, M Saunders, Y Sun. NIPS 2016.

Evaluating the statistical significance of biclusters
JD Lee, Y Sun, JE Taylor. NIPS 2015.

Learning Mixtures of Linear Classifiers
Y Sun, S Ioannidis, A Montanari. ICML 2014.

Technical reports

An inexact subsampled proximal Newton-type method for large-scale machine learning
X Liu, CJ Hsieh, JD Lee, Y Sun.

Valid post-correction inference for censored regression problems
Y Sun, JE Taylor.