Han Liu

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...
09/19/2018 ∙ by Peng Sun, et al. ∙ 10 ∙ share

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Fully Implicit Online Learning

Regularized online learning is widely used in machine learning. In this ...
09/25/2018 ∙ by Chaobing Song, et al. ∙ 8 ∙ share

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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...
10/31/2018 ∙ by Yi Zhen, et al. ∙ 8 ∙ share

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"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans

To support human decision making with machine learning models, we often ...
01/14/2020 ∙ by Vivian Lai, et al. ∙ 8 ∙ share

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Attributed Graph Clustering via Adaptive Graph Convolution

Attributed graph clustering is challenging as it requires joint modellin...
06/04/2019 ∙ by Xiaotong Zhang, et al. ∙ 6 ∙ share

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More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning

We consider the weakly supervised binary classification problem where th...
07/14/2019 ∙ by Xinyang Yi, et al. ∙ 6 ∙ share

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Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning

The quality control of fetal sonographic (FS) images is essential for th...
12/11/2019 ∙ by Hong Luo, et al. ∙ 6 ∙ share

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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 ...
08/29/2018 ∙ by Chris Junchi Li, et al. ∙ 4 ∙ share

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Super-pixel cloud detection using Hierarchical Fusion CNN

Cloud detection plays a very important role in the process of remote sen...
10/19/2018 ∙ by Han Liu, et al. ∙ 4 ∙ share

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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...
09/27/2019 ∙ by Han Liu, et al. ∙ 3 ∙ share

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Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

Solving statistical learning problems often involves nonconvex optimizat...
08/29/2018 ∙ by Chris Junchi Li, et al. ∙ 2 ∙ share

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A convex formulation for high-dimensional sparse sliced inverse regression

Sliced inverse regression is a popular tool for sufficient dimension red...
09/17/2018 ∙ by Kean Ming Tan, et al. ∙ 2 ∙ share

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Finite-Sample Analyses for Fully Decentralized Multi-Agent Reinforcement Learning

Despite the increasing interest in multi-agent reinforcement learning (M...
12/06/2018 ∙ by Kaiqing Zhang, et al. ∙ 2 ∙ share

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Fast Low-rank Metric Learning for Large-scale and High-dimensional Data

Low-rank metric learning aims to learn better discrimination of data sub...
09/13/2019 ∙ by Han Liu, et al. ∙ 2 ∙ share

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Attributed Graph Learning with 2-D Graph Convolution

Graph convolutional neural networks have demonstrated promising performa...
09/26/2019 ∙ by Qimai Li, et al. ∙ 2 ∙ share

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On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models

We consider estimating the parametric components of semi-parametric mult...
09/26/2017 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs

We develop a new modeling framework for Inter-Subject Analysis (ISA). Th...
09/20/2017 ∙ by Cong Ma, et al. ∙ 0 ∙ share

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Property Testing in High Dimensional Ising models

This paper explores the information-theoretic limitations of graph prope...
09/20/2017 ∙ by Matey Neykov, et al. ∙ 0 ∙ share

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Adaptive Inferential Method for Monotone Graph Invariants

We consider the problem of undirected graphical model inference. In many...
07/28/2017 ∙ by Junwei Lu, et al. ∙ 0 ∙ share

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Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models

We consider the problem of learning high-dimensional Gaussian graphical ...
06/04/2017 ∙ by Qiang Sun, et al. ∙ 0 ∙ share

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Continual Learning in Generative Adversarial Nets

Developments in deep generative models have allowed for tractable learni...
05/23/2017 ∙ by Ari Seff, et al. ∙ 0 ∙ share

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Homotopy Parametric Simplex Method for Sparse Learning

High dimensional sparse learning has imposed a great computational chall...
04/04/2017 ∙ by Haotian Pang, et al. ∙ 0 ∙ share

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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...
12/29/2016 ∙ by Xingguo Li, et al. ∙ 0 ∙ share

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Max-Norm Optimization for Robust Matrix Recovery

This paper studies the matrix completion problem under arbitrary samplin...
09/24/2016 ∙ by Ethan X. Fang, et al. ∙ 0 ∙ share

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Sparse Tensor Graphical Model: Non-convex Optimization and Statistical Inference

We consider the estimation and inference of sparse graphical models that...
09/15/2016 ∙ by Will Wei Sun, et al. ∙ 0 ∙ share

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Combinatorial Inference for Graphical Models

We propose a new family of combinatorial inference problems for graphica...
08/10/2016 ∙ by Matey Neykov, et al. ∙ 0 ∙ share

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On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization

The cyclic block coordinate descent-type (CBCD-type) methods, which perf...
07/10/2016 ∙ by Xingguo Li, et al. ∙ 0 ∙ share

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A First Order Free Lunch for SQRT-Lasso

Many statistical machine learning techniques sacrifice convenient comput...
05/25/2016 ∙ by Xingguo Li, et al. ∙ 0 ∙ share

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Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction

We propose a stochastic variance reduced optimization algorithm for solv...
05/09/2016 ∙ by Xingguo Li, et al. ∙ 0 ∙ share

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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...
04/29/2016 ∙ by Kean Ming Tan, et al. ∙ 0 ∙ share

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Near-Optimal Stochastic Approximation for Online Principal Component Estimation

Principal component analysis (PCA) has been a prominent tool for high-di...
03/16/2016 ∙ by Chris Junchi Li, et al. ∙ 0 ∙ share

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Sharp Computational-Statistical Phase Transitions via Oracle Computational Model

We study the fundamental tradeoffs between computational tractability an...
12/30/2015 ∙ by Zhaoran Wang, et al. ∙ 0 ∙ share

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Post-Regularization Inference for Dynamic Nonparanormal Graphical Models

We propose a novel class of dynamic nonparanormal graphical models, whic...
12/28/2015 ∙ by Junwei Lu, et al. ∙ 0 ∙ share

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Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference

We study parameter estimation and asymptotic inference for sparse nonlin...
11/14/2015 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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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...
10/30/2015 ∙ by Matey Neykov, et al. ∙ 0 ∙ share

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Optimal linear estimation under unknown nonlinear transform

Linear regression studies the problem of estimating a model parameter β^...
05/13/2015 ∙ by Xinyang Yi, et al. ∙ 0 ∙ share

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Graphical Fermat's Principle and Triangle-Free Graph Estimation

We consider the problem of estimating undirected triangle-free graphs of...
04/23/2015 ∙ by Junwei Lu, et al. ∙ 0 ∙ share

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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...
03/18/2015 ∙ by Yan Li, et al. ∙ 0 ∙ share

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Post-Regularization Confidence Bands for High Dimensional Nonparametric Models with Local Sparsity

We propose a novel high dimensional nonparametric model named ATLAS whic...
03/10/2015 ∙ by Junwei Lu, et al. ∙ 0 ∙ share

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Statistical Limits of Convex Relaxations

Many high dimensional sparse learning problems are formulated as nonconv...
03/04/2015 ∙ by Zhaoran Wang, et al. ∙ 0 ∙ share

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An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices

There has been an increasing interest in testing the equality of large P...
02/11/2015 ∙ by Cheng Zhou, et al. ∙ 0 ∙ share

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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...
02/09/2015 ∙ by Quanquan Gu, et al. ∙ 0 ∙ share

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Provable Sparse Tensor Decomposition

We propose a novel sparse tensor decomposition method, namely Tensor Tru...
02/05/2015 ∙ by Will Wei Sun, et al. ∙ 0 ∙ share

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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...
12/30/2014 ∙ by Yang Ning, et al. ∙ 0 ∙ share

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High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality

We provide a general theory of the expectation-maximization (EM) algorit...
12/30/2014 ∙ by Zhaoran Wang, et al. ∙ 0 ∙ share

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A General Framework for Robust Testing and Confidence Regions in High-Dimensional Quantile Regression

We propose a robust inferential procedure for assessing uncertainties of...
12/30/2014 ∙ by Tianqi Zhao, et al. ∙ 0 ∙ share

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On Semiparametric Exponential Family Graphical Models

We propose a new class of semiparametric exponential family graphical mo...
12/30/2014 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory

The pathwise coordinate optimization is one of the most important comput...
12/23/2014 ∙ by Tuo Zhao, et al. ∙ 0 ∙ share

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Testing and Confidence Intervals for High Dimensional Proportional Hazards Model

This paper proposes a decorrelation-based approach to test hypotheses an...
12/16/2014 ∙ by Ethan X. Fang, et al. ∙ 0 ∙ share

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A Likelihood Ratio Framework for High Dimensional Semiparametric Regression

We propose a likelihood ratio based inferential framework for high dimen...
12/06/2014 ∙ by Yang Ning, et al. ∙ 0 ∙ share

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Han Liu

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