DC2: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering. - Ke Alexander Wang; Xinran Bian; Pan Liu; Donghui Yan (IEEE BigData, 2019)

Exact Gaussian Processes on a Million Data Points. - Ke Alexander Wang; Geoff Pleiss; Jacob R. Gardner; Stephen Tyree; Kilian Q. Weinberger; Andrew Gordon Wilson (NeurIPS, 2019)

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. - Marc Finzi; Ke Alexander Wang; Andrew Gordon Wilson (NeurIPS, 2020)

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. - Ke Alexander Wang; Danielle C. Maddix; Yuyang Wang 001 (ICBINB@NeurIPS, 2021)

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. - Sanyam Kapoor; Marc Finzi; Ke Alexander Wang; Andrew Gordon Wilson (ICML, 2021)

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. - Willie Neiswanger; Ke Alexander Wang; Stefano Ermon (ICML, 2021)

Is Importance Weighting Incompatible with Interpolating Classifiers? - Ke Alexander Wang; Niladri Shekhar Chatterji; Saminul Haque; Tatsunori Hashimoto (ICLR, 2022)

Sequence Modeling with Multiresolution Convolutional Memory. - Jiaxin Shi; Ke Alexander Wang; Emily B. Fox (ICML, 2023)

Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild. - Ke Alexander Wang; Emily B. Fox (ML4H@NeurIPS, 2023)

Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates. - Ke Alexander Wang; Matthew E. Levine; Jiaxin Shi; Emily B. Fox (CoRR, 2023)

Test-time regression: a unifying framework for designing sequence models with associative memory. - Ke Alexander Wang; Jiaxin Shi; Emily B. Fox (CoRR, 2025)