Kronecker sum decompositions of space-time data. - Kristjan H. Greenewald; Theodoros Tsiligkaridis; Alfred O. Hero III (CAMSAP, 2013)

Regularized block Toeplitz covariance matrix estimation via Kronecker product expansions. - Kristjan H. Greenewald; Alfred O. Hero III (SSP, 2014)

Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification. - Kristjan H. Greenewald; Alfred O. Hero III (CoRR, 2014)

Robust Kronecker Product PCA for Spatio-Temporal Covariance Estimation. - Kristjan H. Greenewald; Alfred O. Hero III (IEEE Trans. Signal Process., 2015)

Dynamic metric learning from pairwise comparisons. - Kristjan H. Greenewald; Stephen Kelley; Alfred O. Hero III (Allerton, 2016)

Improving convergence of divergence functional ensemble estimators. - Kevin R. Moon; Kumar Sricharan; Kristjan H. Greenewald; Alfred O. Hero III (ISIT, 2016)

Distance Metric Tracking. - Kristjan H. Greenewald; Stephen Kelley; Alfred O. Hero III (CoRR, 2016)

Robust SAR STAP via Kronecker decomposition. - Kristjan H. Greenewald; Edmund G. Zelnio; Alfred O. Hero III (IEEE Trans. Aerosp. Electron. Syst., 2016)

Time-dependent spatially varying graphical models, with application to brain fMRI data analysis. - Kristjan H. Greenewald; Seyoung Park; Shuheng Zhou 002; Alexander Giessing (NIPS, 2017)

Action Centered Contextual Bandits. - Kristjan H. Greenewald; Ambuj Tewari; Susan A. Murphy; Predrag V. Klasnja (NIPS, 2017)

Similarity Function Tracking Using Pairwise Comparisons. - Kristjan H. Greenewald; Stephen Kelley; Brandon Oselio; Alfred O. Hero III (IEEE Trans. Signal Process., 2017)

Estimating Information Flow in Neural Networks. - Ziv Goldfeld; Ewout van den Berg; Kristjan H. Greenewald; Igor Melnyk; Nam Nguyen; Brian Kingsbury; Yury Polyanskiy (CoRR, 2018)

Ensemble Estimation of Information Divergence †. - Kevin R. Moon; Kumar Sricharan; Kristjan H. Greenewald; Alfred O. Hero III (Entropy, 2018)

Estimating Information Flow in Deep Neural Networks. - Ziv Goldfeld; Ewout van den Berg; Kristjan H. Greenewald; Igor Melnyk; Nam Nguyen; Brian Kingsbury; Yury Polyanskiy (ICML, 2019)

Bayesian Nonparametric Federated Learning of Neural Networks. - Mikhail Yurochkin; Mayank Agarwal; Soumya Ghosh; Kristjan H. Greenewald; Trong Nghia Hoang; Yasaman Khazaeni (ICML, 2019)

Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions. - Ziv Goldfeld; Kristjan H. Greenewald; Jonathan Weed; Yury Polyanskiy (ISIT, 2019)

Sample Efficient Active Learning of Causal Trees. - Kristjan H. Greenewald; Dmitriy Katz; Karthikeyan Shanmugam; Sara Magliacane; Murat Kocaoglu; Enric Boix Adserà; Guy Bresler (NeurIPS, 2019)

Statistical Model Aggregation via Parameter Matching. - Mikhail Yurochkin; Mayank Agarwal; Soumya Ghosh; Kristjan H. Greenewald; Trong Nghia Hoang (NeurIPS, 2019)

BreGMN: scaled-Bregman Generative Modeling Networks. - Akash Srivastava; Kristjan H. Greenewald; Farzaneh Mirzazadeh (CoRR, 2019)

Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency. - Ziv Goldfeld; Kristjan H. Greenewald (AISTATS, 2020)

Entropic Causal Inference: Identifiability and Finite Sample Results. - Spencer Compton; Murat Kocaoglu; Kristjan H. Greenewald; Dmitriy Katz (NeurIPS, 2020)

Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance. - Ziv Goldfeld; Kristjan H. Greenewald; Kengo Kato (NeurIPS, 2020)

Active Structure Learning of Causal DAGs via Directed Clique Trees. - Chandler Squires; Sara Magliacane; Kristjan H. Greenewald; Dmitriy Katz; Murat Kocaoglu; Karthikeyan Shanmugam (NeurIPS, 2020)

The Computational Limits of Deep Learning. - Neil C. Thompson; Kristjan H. Greenewald; Keeheon Lee; Gabriel F. Manso (CoRR, 2020)

Active Structure Learning of Causal DAGs via Directed Clique Tree. - Chandler Squires; Sara Magliacane; Kristjan H. Greenewald; Dmitriy Katz; Murat Kocaoglu; Karthikeyan Shanmugam (CoRR, 2020)

High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation. - Kristjan H. Greenewald; Dmitriy A. Katz-Rogozhnikov; Karthik Shanmugam (CoRR, 2020)

Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity. - Peng Liao; Kristjan H. Greenewald; Predrag V. Klasnja; Susan A. Murphy (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2020)

Convergence of Smoothed Empirical Measures With Applications to Entropy Estimation. - Ziv Goldfeld; Kristjan H. Greenewald; Jonathan Niles-Weed; Yury Polyanskiy (IEEE Trans. Inf. Theory, 2020)

High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation. - Kristjan H. Greenewald; Karthikeyan Shanmugam; Dmitriy A. Katz-Rogozhnikov (AISTATS, 2021)

Measuring Generalization with Optimal Transport. - Ching-Yao Chuang; Youssef Mroueh; Kristjan H. Greenewald; Antonio Torralba 001; Stefanie Jegelka (NeurIPS, 2021)

Sliced Mutual Information: A Scalable Measure of Statistical Dependence. - Ziv Goldfeld; Kristjan H. Greenewald (NeurIPS, 2021)

Improving approximate optimal transport distances using quantization. - Gaspard Beugnot; Aude Genevay; Kristjan H. Greenewald; Justin Solomon 001 (UAI, 2021)

Entropic Causal Inference: Graph Identifiability. - Spencer Compton; Kristjan H. Greenewald; Dmitriy A. Katz; Murat Kocaoglu (ICML, 2022)

Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets. - Tal Shnitzer; Mikhail Yurochkin; Kristjan H. Greenewald; Justin M. Solomon (ICML, 2022)

$k$-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension. - Ziv Goldfeld; Kristjan H. Greenewald; Theshani Nuradha; Galen Reeves (NeurIPS, 2022)

Outlier-Robust Group Inference via Gradient Space Clustering. - Yuchen Zeng; Kristjan H. Greenewald; Kangwook Lee 001; Justin Solomon 001; Mikhail Yurochkin (CoRR, 2022)

$k$-Variance: A Clustered Notion of Variance. - Justin Solomon 001; Kristjan H. Greenewald; Haikady N. Nagaraja (SIAM J. Math. Data Sci., 2022)

Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier. - Spencer Compton; Dmitriy Katz; Benjamin Qi; Kristjan H. Greenewald; Murat Kocaoglu (AISTATS, 2023)

Learning Proximal Operators to Discover Multiple Optima. - Lingxiao Li; Noam Aigerman; Vladimir G. Kim; Jiajin Li; Kristjan H. Greenewald; Mikhail Yurochkin; Justin Solomon 001 (ICLR, 2023)

High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction. - Kristjan H. Greenewald; Brian Kingsbury; Yuancheng Yu (ISIT, 2023)

Max-Sliced Mutual Information. - Dor Tsur; Ziv Goldfeld; Kristjan H. Greenewald (NeurIPS, 2023)

Post-processing Private Synthetic Data for Improving Utility on Selected Measures. - Hao Wang; Shivchander Sudalairaj; John Henning; Kristjan H. Greenewald; Akash Srivastava (NeurIPS, 2023)

Identifiability Guarantees for Causal Disentanglement from Soft Interventions. - Jiaqi Zhang; Kristjan H. Greenewald; Chandler Squires; Akash Srivastava; Karthikeyan Shanmugam; Caroline Uhler (NeurIPS, 2023)

Risk Assessment and Statistical Significance in the Age of Foundation Models. - Apoorva Nitsure; Youssef Mroueh; Mattia Rigotti; Kristjan H. Greenewald; Brian Belgodere; Mikhail Yurochkin; Jirí Navrátil 001; Igor Melnyk; Jerret Ross (CoRR, 2023)

k-Mixup Regularization for Deep Learning via Optimal Transport. - Kristjan H. Greenewald; Anming Gu; Mikhail Yurochkin; Justin Solomon 001; Edward Chien (Trans. Mach. Learn. Res., 2023)

Slicing Mutual Information Generalization Bounds for Neural Networks. - Kimia Nadjahi; Kristjan H. Greenewald; Rickard Brüel Gabrielsson; Justin Solomon 001 (ICML, 2024)

Risk Aware Benchmarking of Large Language Models. - Apoorva Nitsure; Youssef Mroueh; Mattia Rigotti; Kristjan H. Greenewald; Brian Belgodere; Mikhail Yurochkin; Jirí Navrátil 001; Igor Melnyk; Jarret Ross (ICML, 2024)

Thermometer: Towards Universal Calibration for Large Language Models. - Maohao Shen; Subhro Das; Kristjan H. Greenewald; Prasanna Sattigeri; Gregory W. Wornell; Soumya Ghosh (ICML, 2024)

Asymmetry in Low-Rank Adapters of Foundation Models. - Jiacheng Zhu; Kristjan H. Greenewald; Kimia Nadjahi; Haitz Sáez de Ocáriz Borde; Rickard Brüel Gabrielsson; Leshem Choshen; Marzyeh Ghassemi; Mikhail Yurochkin; Justin Solomon 001 (ICML, 2024)

Sharper Rates of Convergence for the Tensor Graphical Lasso Estimator. - Shuheng Zhou 002; Kristjan H. Greenewald (ISIT, 2024)

Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training. - Kristjan H. Greenewald; Yuancheng Yu; Hao Wang; Kai Xu (NeurIPS, 2024)

Score Distillation via Reparametrized DDIM. - Artem Lukoianov; Haitz Sáez de Ocáriz Borde; Kristjan H. Greenewald; Vitor Guizilini; Timur M. Bagautdinov; Vincent Sitzmann; Justin M. Solomon (NeurIPS, 2024)

Distributional Preference Alignment of LLMs via Optimal Transport. - Igor Melnyk; Youssef Mroueh; Brian Belgodere; Mattia Rigotti; Apoorva Nitsure; Mikhail Yurochkin; Kristjan H. Greenewald; Jirí Navrátil 001; Jarret Ross (NeurIPS, 2024)

Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking. - Gabriel Rioux; Apoorva Nitsure; Mattia Rigotti; Kristjan H. Greenewald; Youssef Mroueh (NeurIPS, 2024)

PresAIse, A Prescriptive AI Solution for Enterprises. - Wei Sun 031; Scott McFaddin; Linh Ha Tran; Shivaram Subramanian; Kristjan H. Greenewald; Yeshi Tenzin; Zack Xue; Youssef Drissi; Markus Ettl (CoRR, 2024)

Synthetic Census Data Generation via Multidimensional Multiset Sum. - Cynthia Dwork; Kristjan H. Greenewald; Manish Raghavan (CoRR, 2024)

Distributional Preference Alignment of LLMs via Optimal Transport. - Igor Melnyk; Youssef Mroueh; Brian Belgodere; Mattia Rigotti; Apoorva Nitsure; Mikhail Yurochkin; Kristjan H. Greenewald; Jirí Navrátil 001; Jerret Ross (CoRR, 2024)

Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead. - Rickard Brüel Gabrielsson; Jiacheng Zhu; Onkar Bhardwaj; Leshem Choshen; Kristjan H. Greenewald; Mikhail Yurochkin; Justin Solomon 001 (CoRR, 2024)

Domain Adaptable Prescriptive AI Agent for Enterprise. - Piero Orderique; Wei Sun; Kristjan H. Greenewald (CoRR, 2024)

PresAIse, a prescriptive AI solution for enterprise. - Wei Sun 031; Scott McFaddin; Linh Ha Tran; Shivaram Subramanian; Kristjan H. Greenewald; Yeshi Tenzin; Zack Xue; Youssef Drissi; Markus Ettl (INFOR Inf. Syst. Oper. Res., 2024)

Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior. - Anming Gu; Edward Chien; Kristjan H. Greenewald (ICLR, 2025)

Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection. - Yihao Xue; Kristjan H. Greenewald; Youssef Mroueh; Baharan Mirzasoleiman (CoRR, 2025)

Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training. - Youssef Mroueh; Nicolas Dupuis; Brian Belgodere; Apoorva Nitsure; Mattia Rigotti; Kristjan H. Greenewald; Jirí Navrátil 001; Jerret Ross; Jesus Rios (CoRR, 2025)

Know What You Don't Know: Uncertainty Calibration of Process Reward Models. - Young-Jin Park; Kristjan H. Greenewald; Kaveh Alim; Hao Wang; Navid Azizan (CoRR, 2025)

Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics. - Anming Gu; Edward Chien; Kristjan H. Greenewald (CoRR, 2025)