
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Starcraft II (SCII) is widely considered as the most challenging Real Ti...
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Neural Machine Translation with AdequacyOriented Learning
Although Neural Machine Translation (NMT) models have advanced stateof...
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NATTACK: Learning the Distributions of Adversarial Examples for an Improved BlackBox Attack on Deep Neural Networks
Powerful adversarial attack methods are vital for understanding how to c...
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Fully Implicit Online Learning
Regularized online learning is widely used in machine learning. In this ...
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Video Relocalization
Many methods have been developed to help people find the video contents ...
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Diffusion Approximations for Online Principal Component Estimation and Global Convergence
In this paper, we propose to adopt the diffusion approximation tools to ...
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SuperIdentity Convolutional Neural Network for Face Hallucination
Face hallucination is a generative task to superresolve the facial imag...
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Endtoend Active Object Tracking and Its Realworld Deployment via Reinforcement Learning
We study active object tracking, where a tracker takes visual observatio...
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A convex formulation for highdimensional sparse sliced inverse regression
Sliced inverse regression is a popular tool for sufficient dimension red...
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Crossdatabase nonfrontal facial expression recognition based on transductive deep transfer learning
Crossdatabase nonfrontal expression recognition is a very meaningful b...
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FiniteSample Analyses for Fully Decentralized MultiAgent Reinforcement Learning
Despite the increasing interest in multiagent reinforcement learning (M...
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Candidates v.s. Noises Estimation for Large MultiClass Classification Problem
This paper proposes a method for multiclass classification problems, wh...
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Deep Subspace Clustering Networks
We present a novel deep neural network architecture for unsupervised sub...
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Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations
We present novel minibatch stochastic optimization methods for empirical...
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On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions
We propose a DC proximal Newton algorithm for solving nonconvex regulari...
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Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models
We consider the problem of learning highdimensional Gaussian graphical ...
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Deep manifoldtomanifold transforming network for action recognition
Symmetric positive definite (SPD) matrices (e.g., covariances, graph Lap...
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SpatialTemporal Recurrent Neural Network for Emotion Recognition
Emotion analysis is a crucial problem to endow artifact machines with re...
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Multiscale Convolutional Neural Networks for Crowd Counting
Crowd counting on static images is a challenging problem due to scale va...
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Efficient Distributed Learning with Sparsity
We propose a novel, efficient approach for distributed sparse learning i...
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Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Sparse generalized eigenvalue problem plays a pivotal role in a large fa...
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Local Uncertainty Sampling for LargeScale MultiClass Logistic Regression
A major challenge for building statistical models in the big data era is...
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A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
In modern largescale machine learning applications, the training data a...
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NearOptimal Stochastic Approximation for Online Principal Component Estimation
Principal component analysis (PCA) has been a prominent tool for highdi...
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Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
We study parameter estimation and asymptotic inference for sparse nonlin...
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Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion
Lowrank matrix completion is an important problem with extensive realw...
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Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory
The pathwise coordinate optimization is one of the most important comput...
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Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling
Stochastic Gradient Descent (SGD) is a popular optimization method which...
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A Proximal Stochastic Gradient Method with Progressive Variance Reduction
We consider the problem of minimizing the sum of two convex functions: o...
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Stochastic Optimization with Importance Sampling
Uniform sampling of training data has been commonly used in traditional ...
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Communication Efficient Distributed Optimization using an Approximate Newtontype Method
We present a novel Newtontype method for distributed optimization, whic...
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Adaptive Stochastic Alternating Direction Method of Multipliers
The Alternating Direction Method of Multipliers (ADMM) has been studied ...
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Gradient Hard Thresholding Pursuit for SparsityConstrained Optimization
Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedu...
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Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics
We investigate a generic problem of learning pairwise exponential family...
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Highdimensional Joint Sparsity Random Effects Model for Multitask Learning
Joint sparsity regularization in multitask learning has attracted much ...
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Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization
We introduce a proximal version of the stochastic dual coordinate ascent...
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Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
We provide theoretical analysis of the statistical and computational pro...
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Accelerated MiniBatch Stochastic Dual Coordinate Ascent
Stochastic dual coordinate ascent (SDCA) is an effective technique for s...
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Learning Sparse LowThreshold Linear Classifiers
We consider the problem of learning a nonnegative linear classifier wit...
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Stochastic Gradient Descent for Nonsmooth Optimization: Convergence Results and Optimal Averaging Schemes
Stochastic Gradient Descent (SGD) is one of the simplest and most popula...
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Analysis of a randomized approximation scheme for matrix multiplication
This note gives a simple analysis of a randomized approximation scheme f...
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Learning Nonlinear Dynamic Models
We present a novel approach for learning nonlinear dynamic models, which...
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Proximal Stochastic Dual Coordinate Ascent
We introduce a proximal version of dual coordinate ascent method. We dem...
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Partial Gaussian Graphical Model Estimation
This paper studies the partial estimation of Gaussian graphical models f...
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Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Stochastic Gradient Descent (SGD) has become popular for solving large s...
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A ProximalGradient Homotopy Method for the Sparse LeastSquares Problem
We consider solving the ℓ_1regularized leastsquares (ℓ_1LS) problem i...
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Deviation optimal learning using greedy Qaggregation
Given a finite family of functions, the goal of model selection aggregat...
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A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems
This paper develops a general theoretical framework to analyze structure...
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Truncated Power Method for Sparse Eigenvalue Problems
This paper considers the sparse eigenvalue problem, which is to extract ...
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Learning Nonlinear Functions Using Regularized Greedy Forest
We consider the problem of learning a forest of nonlinear decision rules...
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Tong Zhang
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Machine learning researcher, and the executive director of Tencent AI Lab