Ke Alexander Wang

According to our database, Ke Alexander Wang authored at least 10 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates.

Ke Alexander Wang, Matthew E. Levine, Jiaxin Shi, Emily B. Fox

CoRR, 2023

Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild.

Ke Alexander Wang, Emily B. Fox

Proceedings of the Machine Learning for Health, 2023

Sequence Modeling with Multiresolution Convolutional Memory.

Jiaxin Shi, Ke Alexander Wang, Emily B. Fox

Proceedings of the International Conference on Machine Learning, 2023

2022

Is Importance Weighting Incompatible with Interpolating Classifiers?

Ke Alexander Wang, Niladri Shekhar Chatterji, Saminul Haque, Tatsunori Hashimoto

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information.

Willie Neiswanger, Ke Alexander Wang, Stefano Ermon

Proceedings of the 38th International Conference on Machine Learning, 2021

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes.

Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

Proceedings of the 38th International Conference on Machine Learning, 2021

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics.

Ke Alexander Wang, Danielle C. Maddix, Yuyang Wang

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

2020

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints.

Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

DC²: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering.

Ke Alexander Wang, Xinran Bian, Pan Liu, Donghui Yan

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019