NIPS Proceedings
^{β}
Books
Tong Zhang
42 Papers
Adaptive Sampling Towards Fast Graph Representation Learning
(2018)
Communication Compression for Decentralized Training
(2018)
Exponentially Weighted Imitation Learning for Batched Historical Data
(2018)
Gradient Sparsification for Communication-Efficient Distributed Optimization
(2018)
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
(2018)
Stochastic Expectation Maximization with Variance Reduction
(2018)
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity
(2018)
Deep Subspace Clustering Networks
(2017)
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
(2017)
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
(2017)
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
(2017)
Exact Recovery of Hard Thresholding Pursuit
(2016)
Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation
(2016)
Local Smoothness in Variance Reduced Optimization
(2015)
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
(2015)
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
(2015)
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
(2013)
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
(2013)
Selective Labeling via Error Bound Minimization
(2012)
Greedy Model Averaging
(2011)
Learning to Search Efficiently in High Dimensions
(2011)
Spectral Methods for Learning Multivariate Latent Tree Structure
(2011)
Agnostic Active Learning Without Constraints
(2010)
Deep Coding Network
(2010)
Multi-Label Prediction via Compressed Sensing
(2009)
Nonlinear Learning using Local Coordinate Coding
(2009)
Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models
(2008)
Multi-stage Convex Relaxation for Learning with Sparse Regularization
(2008)
Sparse Online Learning via Truncated Gradient
(2008)
A General Boosting Method and its Application to Learning Ranking Functions for Web Search
(2007)
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information
(2007)
Learning on Graph with Laplacian Regularization
(2006)
Analysis of Spectral Kernel Design based Semi-supervised Learning
(2005)
Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification
(2004)
Support Vector Classification with Input Data Uncertainty
(2004)
An Infinity-sample Theory for Multi-category Large Margin Classification
(2003)
Learning Bounds for a Generalized Family of Bayesian Posterior Distributions
(2003)
Data-Dependent Bounds for Bayesian Mixture Methods
(2002)
Effective Dimension and Generalization of Kernel Learning
(2002)
Convergence of Large Margin Separable Linear Classification
(2000)
Regularized Winnow Methods
(2000)
Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions
(1999)