69 citations · 193 across the 7 of their papers we have counts for
7 papers · 1 filter
On the Complexity of Learning Neural Networks
Le Song, Santosh Vempala, John Wilmes +1
The stunning empirical successes of neural networks currently lack rigorous theoretical explanation. What form would such an explanation take, in the face of existing complexity-th…
Deep Semi-Random Features for Nonlinear Function Approximation
Kenji Kawaguchi, Bo Xie, Vikas Verma +1
We propose semi-random features for nonlinear function approximation. The flexibility of semi-random feature lies between the fully adjustable units in deep learning and the random…
Diverse Neural Network Learns True Target Functions
Bo Xie, Yingyu Liang, Le Song
Neural networks are a powerful class of functions that can be trained with simple gradient descent to achieve state-of-the-art performance on a variety of applications. Despite the…
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients
Bo Xie, Yingyu Liang, Le Song
Nonlinear component analysis such as kernel Principle Component Analysis (KPCA) and kernel Canonical Correlation Analysis (KCCA) are widely used in machine learning, statistics and…
Communication Efficient Distributed Kernel Principal Component Analysis
Maria-Florina Balcan, Yingyu Liang, Le Song +2
Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional da…
Scalable Kernel Methods via Doubly Stochastic Gradients
Bo Dai, Bo Xie, Niao He +4
The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems. Or have we simply not tried hard enough f…
Nonparametric Estimation of Multi-View Latent Variable Models
Le Song, Animashree Anandkumar, Bo Dai +1
Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However…