270 citations · 862 across the 16 of their papers we have counts for
16 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…
An Alternative View: When Does SGD Escape Local Minima?
Robert Kleinberg, Yuanzhi Li, Yang Yuan
Stochastic gradient descent (SGD) is widely used in machine learning. Although being commonly viewed as a fast but not accurate version of gradient descent (GD), it always finds be…
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…