
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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Fully Implicit Online Learning
Regularized online learning is widely used in machine learning. In this ...
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Performance assessment of the deep learning technologies in grading glaucoma severity
Objective: To validate and compare the performance of eight available de...
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"Why is 'Chicago' deceptive?" Towards Building ModelDriven Tutorials for Humans
To support human decision making with machine learning models, we often ...
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Attributed Graph Clustering via Adaptive Graph Convolution
Attributed graph clustering is challenging as it requires joint modellin...
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More Supervision, Less Computation: StatisticalComputational Tradeoffs in Weakly Supervised Learning
We consider the weakly supervised binary classification problem where th...
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Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning
The quality control of fetal sonographic (FS) images is essential for th...
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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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Superpixel cloud detection using Hierarchical Fusion CNN
Cloud detection plays a very important role in the process of remote sen...
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Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning
Clustering uncertain data is an essential task in data mining for the in...
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Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Solving statistical learning problems often involves nonconvex optimizat...
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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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FiniteSample Analyses for Fully Decentralized MultiAgent Reinforcement Learning
Despite the increasing interest in multiagent reinforcement learning (M...
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Fast Lowrank Metric Learning for Largescale and Highdimensional Data
Lowrank metric learning aims to learn better discrimination of data sub...
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Attributed Graph Learning with 2D Graph Convolution
Graph convolutional neural networks have demonstrated promising performa...
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On Stein's Identity and NearOptimal Estimation in Highdimensional Index Models
We consider estimating the parametric components of semiparametric mult...
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InterSubject Analysis: Inferring Sparse Interactions with Dense IntraGraphs
We develop a new modeling framework for InterSubject Analysis (ISA). Th...
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Property Testing in High Dimensional Ising models
This paper explores the informationtheoretic limitations of graph prope...
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Adaptive Inferential Method for Monotone Graph Invariants
We consider the problem of undirected graphical model inference. In many...
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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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Continual Learning in Generative Adversarial Nets
Developments in deep generative models have allowed for tractable learni...
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Homotopy Parametric Simplex Method for Sparse Learning
High dimensional sparse learning has imposed a great computational chall...
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Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization
We propose a general theory for studying the geometry of nonconvex objec...
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MaxNorm Optimization for Robust Matrix Recovery
This paper studies the matrix completion problem under arbitrary samplin...
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Sparse Tensor Graphical Model: Nonconvex Optimization and Statistical Inference
We consider the estimation and inference of sparse graphical models that...
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Combinatorial Inference for Graphical Models
We propose a new family of combinatorial inference problems for graphica...
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On Faster Convergence of Cyclic Block Coordinate Descenttype Methods for Strongly Convex Minimization
The cyclic block coordinate descenttype (CBCDtype) methods, which perf...
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A First Order Free Lunch for SQRTLasso
Many statistical machine learning techniques sacrifice convenient comput...
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Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
We propose a stochastic variance reduced optimization algorithm for solv...
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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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NearOptimal Stochastic Approximation for Online Principal Component Estimation
Principal component analysis (PCA) has been a prominent tool for highdi...
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Sharp ComputationalStatistical Phase Transitions via Oracle Computational Model
We study the fundamental tradeoffs between computational tractability an...
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PostRegularization Inference for Dynamic Nonparanormal Graphical Models
We propose a novel class of dynamic nonparanormal graphical models, whic...
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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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A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations
We propose a new inferential framework for constructing confidence regio...
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Optimal linear estimation under unknown nonlinear transform
Linear regression studies the problem of estimating a model parameter β^...
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Graphical Fermat's Principle and TriangleFree Graph Estimation
We consider the problem of estimating undirected trianglefree graphs of...
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The Knowledge Gradient Policy Using A Sparse Additive Belief Model
We propose a sequential learning policy for noisy discrete global optimi...
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PostRegularization Confidence Bands for High Dimensional Nonparametric Models with Local Sparsity
We propose a novel high dimensional nonparametric model named ATLAS whic...
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Statistical Limits of Convex Relaxations
Many high dimensional sparse learning problems are formulated as nonconv...
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An ExtremeValue Approach for Testing the Equality of Large UStatistic Based Correlation Matrices
There has been an increasing interest in testing the equality of large P...
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Local and Global Inference for High Dimensional Nonparanormal Graphical Models
This paper proposes a unified framework to quantify local and global inf...
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Provable Sparse Tensor Decomposition
We propose a novel sparse tensor decomposition method, namely Tensor Tru...
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A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models
We consider the problem of uncertainty assessment for low dimensional co...
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High Dimensional ExpectationMaximization Algorithm: Statistical Optimization and Asymptotic Normality
We provide a general theory of the expectationmaximization (EM) algorit...
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A General Framework for Robust Testing and Confidence Regions in HighDimensional Quantile Regression
We propose a robust inferential procedure for assessing uncertainties of...
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On Semiparametric Exponential Family Graphical Models
We propose a new class of semiparametric exponential family graphical mo...
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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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Testing and Confidence Intervals for High Dimensional Proportional Hazards Model
This paper proposes a decorrelationbased approach to test hypotheses an...
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A Likelihood Ratio Framework for High Dimensional Semiparametric Regression
We propose a likelihood ratio based inferential framework for high dimen...
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