(for most recent listing check Google Scholar page)
Z. Goldfeld, K. Greenewald, J. Weed, and Y. Polyanskiy, Convergence of Smoothed Empirical Measures With Applications to Entropy Estimation IEEE Transactions on Information Theory, 2020.
P. Liao, K. Greenewald, P. Klasnja, and S. Murphy, Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020.
K. Greenewald, S. Zhou, A. Hero: Tensor Graphical Lasso (TeraLasso) JRSS Series B, 2019.
K. Moon, K. Sricharan, K. Greenewald, and A. Hero, Ensemble Estimation of Information Divergence Entropy, 2018.
K. Greenewald, S. Kelley, B. Oselio, and A. Hero: Similarity Function Tracking Using Pairwise Comparisons IEEE Transactions on Signal Processing, 2017.
K. Greenewald, E. Zelnio, and A. Hero: Robust SAR STAP via Kronecker decomposition (IEEE Transactions on Aerospace and Electronic Systems 2017).
K. Greenewald and A. Hero: Robust Kronecker Product PCA for Spatio-Temporal Covariance Estimation (IEEE Transactions on Signal Processing 2015).
G. Beugnot, A. Genevay, J. Solomon, and K. Greenewald, Efficient Stochastic Approximation of Optimal Transport Distances Uncertainty in Artificial Intelligence (UAI), 2021.
K. Greenewald, K. Shanmugam, and D. Katz-Rogozhnikov, High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation AISTATS, 2021.
Z. Goldfeld, K. Greenewald, and K. Kato, Asymptotic Guarantees for Generative Modeling based on the Smooth Wasserstein Distance NeurIPS, 2020.
S. Compton, M. Kocaoglu, K. Greenewald, and D. Katz, Entropic Causal Inference: Identifiability and Finite Sample Results NeurIPS, 2020.
C. Squires, S. Magliacane, K. Greenewald, D. Katz, M. Kocaoglu, and K. Shanmugam, Active Structure Learning of Causal DAGs via Directed Clique Trees NeurIPS, 2020.
Z. Goldfeld, K. Greenewald, and Y. Polyanskiy, Gaussian Smoothed Optimal Transport: Metric Structure and Statistical Efficiency AISTATS, 2020.
K. Greenewald, D. Katz, M. Kocaoglu, K. Shanmugam, S. Magliacane, G. Bresler, E. Boix, Sample Efficient Active Learning of Causal Trees NeurIPS, 2019.
Z. Goldfeld, E. Van den Berg, K. Greenewald, B. Kingsbury, I. Melnyk, N. Nguyen, and Y. Polyanskiy, Estimating Information Flow in DNNs ICML, 2019.
M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, N. Hoang, and Y. Khazaeni, Bayesian Non-parametric Federated Learning of Neural Networks ICML, 2019.
Z. Goldfeld, K. Greenewald, J. Weed, and Y. Polyanskiy, Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions IEEE International Symposium on Information Theory, 2019.
K. Greenewald, A. Tewari, P. Klesnja, S. Murphy: Action Centered Contextual Bandits NIPS, 2017.
K. Greenewald, S. Park, A. Giessing, and S. Zhou: Time-Varying Matrix-Variate Graphical Models NIPS, 2017.
K. Greenewald, S. Kelley, and A. Hero: Dynamic metric learning from pairwise comparisons (54th Annual Allerton Conference on Communication, Control, and Computing 2016) (invited).
K. Moon, K. Sricharan, K. Greenewald, and A. Hero: Improving Convergence of Divergence Functional Ensemble Estimators (IEEE International Symposium on Information Theory 2016).
K. Greenewald, E. Zelnio, and A. Hero: Kronecker STAP and SAR GMTI (Proceedings of SPIE 2016).
K. Greenewald and A. Hero: Regularized Block Toeplitz Covariance Matrix Estimation via Kronecker Product Expansions (IEEE Workshop on Statistical Signal Processing (SSP) 2014) (invited).
K. Greenewald and A. Hero: Robust Kronecker Product PCA for Spatio-Temporal Covariance Estimation (International Conference on Partial Least Squares and Related Methods (PLS) 2014) (invited).
K. Greenewald and A. Hero: Kronecker PCA based spatio-temporal modeling of video for dismount classification (Proceedings of SPIE, 2014).
K. Greenewald, T. Tsiligkaridis, and A. Hero: Kronecker Sum Decompositions of Space-Time Data (IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) 2013) (invited).
K. Greenewald: High Dimensional Covariance Estimation for Spatio-Temporal Processes PhD Thesis, University of Michigan, January 2017.
K. Greenewald: Prediction of Optimal Bayesian Classification Performance for LADAR ATR MS Thesis, Wright State University, August 2012.