184 citations · 266 across the 4 of their papers we have counts for
6 papers
Learning-Based Low-Rank Approximations
Piotr Indyk, Ali Vakilian, Yang Yuan
We introduce a "learning-based" algorithm for the low-rank decomposition problem: given an matrix , and a parameter , compute a rank- matrix that minimiz…
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Haowei He, Gao Huang, Yang Yuan
Despite the non-convex nature of their loss functions, deep neural networks are known to generalize well when optimized with stochastic gradient descent (SGD). Recent work conjectu…
Hyperparameter Optimization: A Spectral Approach
Elad Hazan, Adam Klivans, Yang Yuan
We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the ca…
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Yuanzhi Li, Yang Yuan
In recent years, stochastic gradient descent (SGD) based techniques has become the standard tools for training neural networks. However, formal theoretical understanding of why SGD…
Simultaneous Nearest Neighbor Search
Piotr Indyk, Robert Kleinberg, Sepideh Mahabadi +1
Motivated by applications in computer vision and databases, we introduce and study the Simultaneous Nearest Neighbor Search (SNN) problem. Given a set of data points, the goal of S…
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition
Rong Ge, Furong Huang, Chi Jin +1
We analyze stochastic gradient descent for optimizing non-convex functions. In many cases for non-convex functions the goal is to find a reasonable local minimum, and the main conc…