Ke Alexander Wang

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Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
Kilian WeinbergerCornell UniversityVerified email at cornell.edu
Geoff PleissColumbia UniversityVerified email at columbia.edu
Jacob GardnerUniversity of PennsylvaniaVerified email at seas.upenn.edu
Stephen TyreeNVIDIAVerified email at nvidia.com
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
Willie NeiswangerPostdoc, Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Tatsunori HashimotoAssistant Professor, StanfordVerified email at stanford.edu
Niladri S. ChatterjiPostdoctoral Researcher, Department of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Sanyam KapoorNew York UniversityVerified email at nyu.edu
Donghui YanVerified email at berkeley.edu
Samuel StantonMachine Learning Scientist, GenentechVerified email at gene.com
Yuyang (Bernie) WangPrincipal Scientist, AWS AI LabsVerified email at mit.edu
Danielle MaddixStanford UniversityVerified email at stanford.edu

Ke Alexander Wang

CS PhD student, Stanford University

Verified email at cs.stanford.edu - Homepage

Machine learning Time-series Probabilistic machine learning Healthcare AI


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Exact Gaussian Processes on a Million Data Points K Wang, G Pleiss, J Gardner, S Tyree, KQ Weinberger, AG Wilson Advances in Neural Information Processing Systems 32, 14648-14659, 2019	179	2019
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints M Finzi, KA Wang, AG Wilson Advances in Neural Information Processing Systems 33, 13880-13889, 2020	72	2020
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information W Neiswanger, KA Wang, S Ermon Proceedings of the 38th International Conference on Machine Learning 139 …, 2021	13	2021
Is Importance Weighting Incompatible with Interpolating Classifiers? KA Wang, NS Chatterji, S Haque, T Hashimoto arXiv preprint arXiv:2112.12986, 2021	9	2021
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes S Kapoor, M Finzi, KA Wang, AG Wilson Proceedings of the 38th International Conference on Machine Learning 139 …, 2021	4	2021
DC²: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering KA Wang, X Bian, P Liu, D Yan 2019 IEEE International Conference on Big Data (Big Data), 5603-5610, 2019	1	2019
Model-based Policy Gradients with Entropy Exploration through Sampling S Stanton, KA Wang, AG Wilson ICML Generative Modeling and Model-Based Reasoning for Robotics and AI Workshop, 2019	1*	2019
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics KA Wang, DC Maddix, B Wang I (Still) Can't Believe It's Not Better! NeurIPS 2021 Workshop, 2021		2021
Large Scale Exact Gaussian Processes Inference and Euclidean Constrained Neural Networks with Physics Priors KA Wang Cornell University, 2020		2020

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