Statistical Learning
Rethinking Kernel Methods for Node Representation Learning on Graphs
Yu Tian, Long Zhao, Xi Peng, Dimitris Metaxas
Thegraphkernelfunction can be utilized toclassify graphs via standard kernel methods such assupport vector machines or k-nearest neighbors.
On the Inductive Bias of Neural Tangent Kernels
Neural Information Processing Systems http://nips.cc/
Efficient Centroid-Linkage Clustering
We also evaluate our algorithm empirically. By leveraging a state-of-the-art nearest-neighbor search library, we obtain a fast and accurate Centroid-Linkage HAC algorithm.
A Step Toward Quantifying Independently Reproducible Machine Learning Research
Neural Information Processing Systems http://nips.cc/
Representation Learning for Treatment Effect Estimation from Observational Data
Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang
Neural Information Processing Systems http://nips.cc/
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Difan Zou, Pan Xu, Quanquan Gu
Figure 3: Ex represents (c)-(d) ConB.
