462 citations · 2.5k across the 127 of their papers we have counts for
19 papers · 1 filter
Rank Aggregation via Heterogeneous Thurstone Preference Models
Tao Jin, Pan Xu, Quanquan Gu +1
We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise dist…
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Pan Xu, Quanquan Gu
Q-learning with neural network function approximation (neural Q-learning for short) is among the most prevalent deep reinforcement learning algorithms. Despite its empirical succes…
Towards Understanding the Spectral Bias of Deep Learning
Yuan Cao, Zhiying Fang, Yue Wu +2
An intriguing phenomenon observed during training neural networks is the spectral bias, which states that neural networks are biased towards learning less complex functions. The pr…
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang +3
Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a…
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks
Yuan Cao, Quanquan Gu
We study the sample complexity of learning one-hidden-layer convolutional neural networks (CNNs) with non-overlapping filters. We propose a novel algorithm called approximate gradi…
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
Bao Wang, Difan Zou, Quanquan Gu +1
As an important Markov Chain Monte Carlo (MCMC) method, stochastic gradient Langevin dynamics (SGLD) algorithm has achieved great success in Bayesian learning and posterior samplin…