Statistical Learning
Chefs ' Random Tables: Non-Trigonometric Random Features
We introduce chefs' random tables (CRTs), a new class of non-trigonometric random features (RFs) to approximate Gaussian and softmax-kernels.
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
Exploiting this property, Daskalakis et al. [2020] builds on the theory of Lin et al. [2020a] and shows
Concrete Score Matching: Generalized Score Matching for Discrete Data
Representing probability distributions by the gradient of their density functions has proven effective in modeling a wide range of continuous data modalities.
Distributed Online Convex Optimization with Compressed Communication Zhipeng T u 1,2, Xi Wang
Since the data are high-dimensional or the network is large-scale, communication load can be a bottleneck for the efficiency of distributed algorithms.
Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds Zhi Gao 1, Y uwei Wu
We use the SGD optimizer in the three training stage.
Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution
For instance, the existence of spurious local minima may not pose any issue if an optimization algorithm can avoid them efficiently.
