Statistical Learning
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Wei Qian, Yuqian Zhang, Yudong Chen
Neural Information Processing Systems http://nips.cc/
Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis Mitchell Ostrow, Adam Eisen, Leo Kozachkov, Ila Fiete
Y et in recurrent networks, computations are implemented at the level of dynamics, and two networks performing the same computation with equivalent dynamics need not exhibit the same geometry.
Foundations of Comparison-Based Hierarchical Clustering
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
Neural Information Processing Systems http://nips.cc/
Generalization Bounds in the Predict-then-Optimize Framework
Othman El Balghiti, Adam Elmachtoub, Paul Grigas, Ambuj Tewari
Neural Information Processing Systems http://nips.cc/
Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation
Shift-and-invert preconditioning, as a classic acceleration technique for the leading eigenvector computation, has received much attention again recently, owing to fast least-squares solvers for efficiently approximating matrix inversions in power iterations.
Exact natural gradient in deep linear networks and its application to the nonlinear case
Alberto Bernacchia, Mate Lengyel, Guillaume Hennequin
Neural Information Processing Systems http://nips.cc/
Tanimoto Random Features for Scalable Molecular Machine Learning
The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kernel.
