Search Results for author: Han Liu

Found 153 papers, 27 papers with code

Bridging the Gap Between Training and Inference of Bayesian Controllable Language Models

no code implementations11 Jun 2022 Han Liu, Bingning Wang, Ting Yao, Haijin Liang, Jianjin Xu, Xiaolin Hu

Large-scale pre-trained language models have achieved great success on natural language generation tasks.

Text Generation

A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism

no code implementations5 Jun 2022 Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Junjie Sun, Hong Yu, Xianchao Zhang

Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data.

Classification Intent Classification +3

Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters

no code implementations23 Mar 2022 Tim Tsz-Kit Lau, Han Liu

On the other hand, in distributionally robust optimization, we seek data-driven decisions which perform well under the most adverse distribution from a nominal distribution constructed from data samples within a certain discrepancy of probability distributions.

Learning to Infer Belief Embedded Communication

no code implementations15 Mar 2022 Guo Ye, Han Liu, Biswa Sengupta

In multi-agent collaboration problems with communication, an agent's ability to encode their intention and interpret other agents' strategies is critical for planning their future actions.

Text Generation

Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning

no code implementations14 Mar 2022 Qinjie Lin, Han Liu, Biswa Sengupta

Our results also demonstrate the advantage of the switch transformer model for absorbing expert knowledge and the importance of value distribution in evaluating the trajectory.

reinforcement-learning

Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data

no code implementations8 Mar 2022 Can Cui, Han Liu, Quan Liu, Ruining Deng, Zuhayr Asad, Yaohong WangShilin Zhao, Haichun Yang, Bennett A. Landman, Yuankai Huo

Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e. g., one or more modalities might not be collected for a patient).

Survival Prediction

Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins

no code implementations4 Mar 2022 Han Liu, Xiaoyu Song, Ge Gao, Hehua Zhang, Yu-Shen Liu, Ming Gu

Semantic rule checking on RDFS/OWL data has been widely used in the construction industry.

Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused Ultrasound Interventions with Conditional Adversarial Networks

no code implementations21 Feb 2022 Han Liu, Michelle K. Sigona, Thomas J. Manuel, Li Min Chen, Charles F. Caskey, Benoit M. Dawant

Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively under MRI guidance.

Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion

no code implementations25 Jan 2022 Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, Benoit M. Dawant

Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.

Unsupervised Domain Adaptation

A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications

no code implementations3 Nov 2021 Xinlei Zhou, Han Liu, Farhad Pourpanah, Tieyong Zeng, XiZhao Wang

This paper provides a comprehensive review of epistemic uncertainty learning techniques in supervised learning over the last five years.

Natural Language Processing

Learning Predictive, Online Approximations of Explanatory, Offline Algorithms

no code implementations29 Sep 2021 Mattson Thieme, Ammar Gilani, Han Liu

In this work, we introduce a general methodology for approximating offline algorithms in online settings.

Multi-Task Learning

Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory

no code implementations ICLR 2022 Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang

This paper proposes a new algorithm for learning the optimal policies under a novel multi-agent predictive state representation reinforcement learning model.

reinforcement-learning

Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

no code implementations13 Sep 2021 Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, Benoit M. Dawant

Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.

Unsupervised Domain Adaptation

Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis

no code implementations CVPR 2021 Xianchao Zhang, Ziyang Cheng, Xiaotong Zhang, Han Liu

In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P2GAN), which promotes generator with the real information in the posterior distribution produced by discriminator.

Image Generation

Review Polarity-wise Recommender

1 code implementation8 Jun 2021 Han Liu, Yangyang Guo, Jianhua Yin, Zan Gao, Liqiang Nie

To be specific, in this model, positive and negative reviews are separately gathered and utilized to model the user-preferred and user-rejected aspects, respectively.

Recommendation Systems

Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading

1 code implementation Findings (ACL) 2021 Zhihan Zhou, Liqian Ma, Han Liu

In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles.

Event Detection Event-Driven Trading +2

Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving

no code implementations21 Mar 2021 Li Wang, Dong Li, Han Liu, Jinzhang Peng, Lu Tian, Yi Shan

Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets.

3D Semantic Segmentation Autonomous Driving +2

BLOCKEYE: Hunting For DeFi Attacks on Blockchain

no code implementations4 Mar 2021 Bin Wang, Han Liu, Chao Liu, Zhiqiang Yang, Qian Ren, Huixuan Zheng, Hong Lei

We applied BLOCKEYE in several popular DeFi projects and managed to discover potential security attacks that are unreported before.

Cryptography and Security Computers and Society

Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue

1 code implementation4 Feb 2021 Han Liu, Caixia Yuan, Xiaojie Wang, Yushu Yang, Huixing Jiang, Zhongyuan Wang

We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue.

Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making

no code implementations13 Jan 2021 Han Liu, Vivian Lai, Chenhao Tan

Although AI holds promise for improving human decision making in societally critical domains, it remains an open question how human-AI teams can reliably outperform AI alone and human alone in challenging prediction tasks (also known as complementary performance).

Decision Making

Morphology Matters: A Multilingual Language Modeling Analysis

1 code implementation11 Dec 2020 Hyunji Hayley Park, Katherine J. Zhang, Coleman Haley, Kenneth Steimel, Han Liu, Lane Schwartz

We fill in missing typological data for several languages and consider corpus-based measures of morphological complexity in addition to expert-produced typological features.

Language Modelling

Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation

no code implementations3 Nov 2020 Han Liu, Can Cui, Dario J. Englot, Benoit M. Dawant

Atlas-based methods are the standard approaches for automatic targeting of the Anterior Nucleus of the Thalamus (ANT) for Deep Brain Stimulation (DBS), but these are known to lack robustness when anatomic differences between atlases and subjects are large.

Label-Wise Document Pre-Training for Multi-Label Text Classification

1 code implementation15 Aug 2020 Han Liu, Caixia Yuan, Xiaojie Wang

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations.

 Ranked #1 on Multi-Label Text Classification on AAPD (Micro F1 metric)

Classification Document Classification +3

The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R

no code implementations27 Jun 2020 Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME).

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

1 code implementation27 Jun 2020 Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao

We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e. g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies.

Sparse Learning

The huge Package for High-dimensional Undirected Graph Estimation in R

no code implementations26 Jun 2020 Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman

We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data.

Model Selection

A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation

no code implementations16 May 2020 Fraser Young, L. Zhang, Richard Jiang, Han Liu, Conor Wall

With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality.

Speech Recognition Transfer Learning

FAME: 3D Shape Generation via Functionality-Aware Model Evolution

1 code implementation9 May 2020 Yanran Guan, Han Liu, Kun Liu, Kangxue Yin, Ruizhen Hu, Oliver van Kaick, Yan Zhang, Ersin Yumer, Nathan Carr, Radomir Mech, Hao Zhang

Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.

Graphics

EQL -- an extremely easy to learn knowledge graph query language, achieving highspeed and precise search

no code implementations19 Mar 2020 Han Liu, Shantao Liu

EQL, also named as Extremely Simple Query Language, can be widely used in the field of knowledge graph, precise search, strong artificial intelligence, database, smart speaker , patent search and other fields.

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

no code implementations14 Jan 2020 Vivian Lai, Han Liu, Chenhao Tan

To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans.

Decision Making

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

no code implementations11 Dec 2019 Hong Luo, Han Liu, Kejun Li, Bo Zhang

An essential criterion for FS image quality control is that all the essential anatomical structures in the section should appear full and remarkable with a clear boundary.

Image Quality Assessment Multi-Task Learning +1

Reconstructing Capsule Networks for Zero-shot Intent Classification

1 code implementation IJCNLP 2019 Han Liu, Xiaotong Zhang, Lu Fan, Xu Fu, i, Qimai Li, Xiao-Ming Wu, Albert Y. S. Lam

With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification.

Classification General Classification +2

Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning

no code implementations27 Sep 2019 Han Liu, Xianchao Zhang, Xiaotong Zhang, Qimai Li, Xiao-Ming Wu

However, there are two issues in existing possible world based algorithms: (1) They rely on all the possible worlds and treat them equally, but some marginal possible worlds may cause negative effects.

Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs

1 code implementation26 Sep 2019 Qimai Li, Xiaotong Zhang, Han Liu, Quanyu Dai, Xiao-Ming Wu

Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that computes the representation of a node by aggregating those of its neighbors.

Graph Representation Learning Node Classification

Attributed Graph Learning with 2-D Graph Convolution

no code implementations25 Sep 2019 Qimai Li, Xiaotong Zhang, Han Liu, Xiao-Ming Wu

Graph convolutional neural networks have demonstrated promising performance in attributed graph learning, thanks to the use of graph convolution that effectively combines graph structures and node features for learning node representations.

Graph Learning Node Classification +1

AdvCodec: Towards A Unified Framework for Adversarial Text Generation

no code implementations25 Sep 2019 Boxin Wang, Hengzhi Pei, Han Liu, Bo Li

In particular, we propose a tree based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation.

Adversarial Text Natural Language Processing +3

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

1 code implementation NeurIPS 2019 Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu

Low-rank metric learning aims to learn better discrimination of data subject to low-rank constraints.

Metric Learning

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

no code implementations NeurIPS 2016 Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu

We consider the weakly supervised binary classification problem where the labels are randomly flipped with probability $1- {\alpha}$.

Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding

no code implementations20 Jun 2019 Yutai Hou, Zhihan Zhou, Yijia Liu, Ning Wang, Wanxiang Che, Han Liu, Ting Liu

It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.

Few-Shot Learning named-entity-recognition +1

Attributed Graph Clustering via Adaptive Graph Convolution

1 code implementation4 Jun 2019 Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu

Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes.

Ranked #3 on Graph Clustering on Cora (NMI metric)

Community Detection Graph Clustering

GLAD: Learning Sparse Graph Recovery

1 code implementation ICLR 2020 Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinvas Aluru, Han Liu, Le Song

Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.

Inductive Bias

Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks

no code implementations28 May 2019 Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu

To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story.

Experimental Design

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

1 code implementation ICLR 2020 Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song

We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces.

Label Efficient Semi-Supervised Learning via Graph Filtering

1 code implementation CVPR 2019 Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan

However, existing graph-based methods either are limited in their ability to jointly model graph structures and data features, such as the classical label propagation methods, or require a considerable amount of labeled data for training and validation due to high model complexity, such as the recent neural-network-based methods.

General Classification Graph Similarity

Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents

no code implementations6 Dec 2018 Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Başar

This work appears to be the first finite-sample analysis for batch MARL, a step towards rigorous theoretical understanding of general MARL algorithms in the finite-sample regime.

Multi-agent Reinforcement Learning reinforcement-learning

Sketching Method for Large Scale Combinatorial Inference

no code implementations NeurIPS 2018 Wei Sun, Junwei Lu, Han Liu

In order to test the hypotheses on their topological structures, we propose two adjacency matrix sketching frameworks: neighborhood sketching and subgraph sketching.

Performance assessment of the deep learning technologies in grading glaucoma severity

no code implementations31 Oct 2018 Yi Zhen, Lei Wang, Han Liu, Jian Zhang, Jiantao Pu

Among these CNNs, the DenseNet had the highest classification accuracy (i. e., 75. 50%) based on pre-trained weights when using global ROIs, as compared to 65. 50% when using local ROIs.

SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images

no code implementations30 Oct 2018 Han Liu, Lei Wang, Yandong Nan, Faguang Jin, Qi. Wang, Jiantao Pu

Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images.

General Classification Thoracic Disease Classification

Super-pixel cloud detection using Hierarchical Fusion CNN

no code implementations19 Oct 2018 Han Liu, Dan Zeng, Qi Tian

Secondly, super-pixel level database is used to train our cloud detection models based on CNN and deep forest.

Cloud Detection General Classification

Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

3 code implementations10 Oct 2018 Jiechao Xiong, Qing Wang, Zhuoran Yang, Peng Sun, Lei Han, Yang Zheng, Haobo Fu, Tong Zhang, Ji Liu, Han Liu

Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely.

reinforcement-learning

Fully Implicit Online Learning

no code implementations25 Sep 2018 Chaobing Song, Ji Liu, Han Liu, Yong Jiang, Tong Zhang

Regularized online learning is widely used in machine learning applications.

online learning

High-Temperature Structure Detection in Ferromagnets

no code implementations21 Sep 2018 Yuan Cao, Matey Neykov, Han Liu

The goal is to distinguish whether the underlying graph is empty, i. e., the model consists of independent Rademacher variables, versus the alternative that the underlying graph contains a subgraph of a certain structure.

TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

3 code implementations19 Sep 2018 Peng Sun, Xinghai Sun, Lei Han, Jiechao Xiong, Qing Wang, Bo Li, Yang Zheng, Ji Liu, Yongsheng Liu, Han Liu, Tong Zhang

Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting.

Decision Making Starcraft +1

A convex formulation for high-dimensional sparse sliced inverse regression

no code implementations17 Sep 2018 Kean Ming Tan, Zhaoran Wang, Tong Zhang, Han Liu, R. Dennis Cook

Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates.

Dimensionality Reduction Variable Selection

Factorized Q-Learning for Large-Scale Multi-Agent Systems

no code implementations11 Sep 2018 Yong Chen, Ming Zhou, Ying Wen, Yaodong Yang, Yufeng Su, Wei-Nan Zhang, Dell Zhang, Jun Wang, Han Liu

Deep Q-learning has achieved a significant success in single-agent decision making tasks.

Multiagent Systems

Diffusion Approximations for Online Principal Component Estimation and Global Convergence

no code implementations NeurIPS 2017 Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang

In this paper, we propose to adopt the diffusion approximation tools to study the dynamics of Oja's iteration which is an online stochastic gradient descent method for the principal component analysis.

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

no code implementations NeurIPS 2016 Chris Junchi Li, Zhaoran Wang, Han Liu

Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory.

Tensor Decomposition

Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval

no code implementations21 Aug 2018 Jianqing Fan, Han Liu, Zhaoran Wang, Zhuoran Yang

We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data.

Graphical Nonconvex Optimization via an Adaptive Convex Relaxation

no code implementations ICML 2018 Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang

Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.

The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference

no code implementations ICML 2018 Hao Lu, Yuan Cao, Zhuoran Yang, Junwei Lu, Han Liu, Zhaoran Wang

We study the hypothesis testing problem of inferring the existence of combinatorial structures in undirected graphical models.

Two-sample testing

Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications

1 code implementation ICLR 2019 Carson Eisenach, Haichuan Yang, Ji Liu, Han Liu

In the former, an agent learns a policy over $\mathbb{R}^d$ and in the latter, over a discrete set of actions each of which is parametrized by a continuous parameter.

Continuous Control

Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models

2 code implementations1 Jun 2018 Carson Eisenach, Han Liu

Compared to the naive interior point method, our method reduces the computational complexity of solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to $\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an $\epsilon$-optimal solution.

Feedback-Based Tree Search for Reinforcement Learning

no code implementations ICML 2018 Daniel R. Jiang, Emmanuel Ekwedike, Han Liu

Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL) technique that iteratively applies MCTS on batches of small, finite-horizon versions of the original infinite-horizon Markov decision process.

Model-based Reinforcement Learning reinforcement-learning

Discrete Factorization Machines for Fast Feature-based Recommendation

1 code implementation6 May 2018 Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang

In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.

Binarization Quantization

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

4 code implementations ICML 2018 Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Başar

To this end, we propose two decentralized actor-critic algorithms with function approximation, which are applicable to large-scale MARL problems where both the number of states and the number of agents are massively large.

Multi-agent Reinforcement Learning reinforcement-learning

The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings

no code implementations23 Jan 2018 Wafa Alorainy, Pete Burnap, Han Liu, Matthew Williams

Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated viathe World Wide Web.

Parametric Simplex Method for Sparse Learning

no code implementations NeurIPS 2017 Haotian Pang, Han Liu, Robert J. Vanderbei, Tuo Zhao

High dimensional sparse learning has imposed a great computational challenge to large scale data analysis.

Sparse Learning

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

no code implementations26 Sep 2017 Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu

We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting.

Property Testing in High Dimensional Ising models

no code implementations20 Sep 2017 Matey Neykov, Han Liu

In terms of methodological development, we propose two types of correlation based tests: computationally efficient screening for ferromagnets, and score type tests for general models, including a fast cycle presence test.

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

no code implementations20 Sep 2017 Cong Ma, Junwei Lu, Han Liu

Our framework is based on the Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of the inter-subject precision matrix.

Adaptive Inferential Method for Monotone Graph Invariants

no code implementations28 Jul 2017 Junwei Lu, Matey Neykov, Han Liu

In this paper, we propose a new inferential framework for testing nested multiple hypotheses and constructing confidence intervals of the unknown graph invariants under undirected graphical models.

CANE: Context-Aware Network Embedding for Relation Modeling

1 code implementation ACL 2017 Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun

Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors.

Community Detection Link Prediction +2

Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models

no code implementations4 Jun 2017 Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang

Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.

Continual Learning in Generative Adversarial Nets

no code implementations23 May 2017 Ari Seff, Alex Beatson, Daniel Suo, Han Liu

Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions.

Continual Learning

Homotopy Parametric Simplex Method for Sparse Learning

no code implementations4 Apr 2017 Haotian Pang, Robert Vanderbei, Han Liu, Tuo Zhao

High dimensional sparse learning has imposed a great computational challenge to large scale data analysis.

Sparse Learning

Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization

no code implementations29 Dec 2016 Xingguo Li, Junwei Lu, Raman Arora, Jarvis Haupt, Han Liu, Zhaoran Wang, Tuo Zhao

We propose a general theory for studying the \xl{landscape} of nonconvex \xl{optimization} with underlying symmetric structures \tz{for a class of machine learning problems (e. g., low-rank matrix factorization, phase retrieval, and deep linear neural networks)}.

Agnostic Estimation for Misspecified Phase Retrieval Models

no code implementations NeurIPS 2016 Matey Neykov, Zhaoran Wang, Han Liu

The goal of noisy high-dimensional phase retrieval is to estimate an $s$-sparse parameter $\boldsymbol{\beta}^*\in \mathbb{R}^d$ from $n$ realizations of the model $Y = (\boldsymbol{X}^{\top} \boldsymbol{\beta}^*)^2 + \varepsilon$.

Blind Attacks on Machine Learners

no code implementations NeurIPS 2016 Alex Beatson, Zhaoran Wang, Han Liu

We study the potential of a “blind attacker” to provably limit a learner’s performance by data injection attack without observing the learner’s training set or any parameter of the distribution from which it is drawn.

Max-Norm Optimization for Robust Matrix Recovery

no code implementations24 Sep 2016 Ethan X. Fang, Han Liu, Kim-Chuan Toh, Wen-Xin Zhou

This paper studies the matrix completion problem under arbitrary sampling schemes.

Matrix Completion

Tensor Graphical Model: Non-convex Optimization and Statistical Inference

no code implementations15 Sep 2016 Xiang Lyu, Will Wei Sun, Zhaoran Wang, Han Liu, Jian Yang, Guang Cheng

We consider the estimation and inference of graphical models that characterize the dependency structure of high-dimensional tensor-valued data.

Combinatorial Inference for Graphical Models

no code implementations10 Aug 2016 Matey Neykov, Junwei Lu, Han Liu

We propose a new family of combinatorial inference problems for graphical models.

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

no code implementations10 Jul 2016 Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong

In particular, we first show that for a family of quadratic minimization problems, the iteration complexity $\mathcal{O}(\log^2(p)\cdot\log(1/\epsilon))$ of the CBCD-type methods matches that of the GD methods in term of dependency on $p$, up to a $\log^2 p$ factor.

On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About Its Nonsmooth Loss Function

no code implementations25 May 2016 Xingguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao

Many machine learning techniques sacrifice convenient computational structures to gain estimation robustness and modeling flexibility.

Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction

no code implementations9 May 2016 Xingguo Li, Raman Arora, Han Liu, Jarvis Haupt, Tuo Zhao

We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints.

Sparse Learning Stochastic Optimization

Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow

no code implementations29 Apr 2016 Kean Ming Tan, Zhaoran Wang, Han Liu, Tong Zhang

Sparse generalized eigenvalue problem (GEP) plays a pivotal role in a large family of high-dimensional statistical models, including sparse Fisher's discriminant analysis, canonical correlation analysis, and sufficient dimension reduction.

Dimensionality Reduction

Near-Optimal Stochastic Approximation for Online Principal Component Estimation

no code implementations16 Mar 2016 Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang

We prove for the first time a nearly optimal finite-sample error bound for the online PCA algorithm.

Sharp Computational-Statistical Phase Transitions via Oracle Computational Model

no code implementations30 Dec 2015 Zhaoran Wang, Quanquan Gu, Han Liu

Based upon an oracle model of computation, which captures the interactions between algorithms and data, we establish a general lower bound that explicitly connects the minimum testing risk under computational budget constraints with the intrinsic probabilistic and combinatorial structures of statistical problems.

Two-sample testing

Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models

no code implementations28 Dec 2015 Junwei Lu, Mladen Kolar, Han Liu

The testing procedures are based on a high dimensional, debiasing-free moment estimator, which uses a novel kernel smoothed Kendall's tau correlation matrix as an input statistic.

Model Selection

Robust Portfolio Optimization

no code implementations NeurIPS 2015 Huitong Qiu, Fang Han, Han Liu, Brian Caffo

We propose a robust portfolio optimization approach based on quantile statistics.

Portfolio Optimization

High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality

no code implementations NeurIPS 2015 Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu

We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models.

Local Smoothness in Variance Reduced Optimization

no code implementations NeurIPS 2015 Daniel Vainsencher, Han Liu, Tong Zhang

Abstract We propose a family of non-uniform sampling strategies to provably speed up a class of stochastic optimization algorithms with linear convergence including Stochastic Variance Reduced Gradient (SVRG) and Stochastic Dual Coordinate Ascent (SDCA).

Stochastic Optimization

Non-convex Statistical Optimization for Sparse Tensor Graphical Model

no code implementations NeurIPS 2015 Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng

We consider the estimation of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data.

Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference

no code implementations14 Nov 2015 Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang

To recover $\beta^*$, we propose an $\ell_1$-regularized least-squares estimator.

A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations

no code implementations30 Oct 2015 Matey Neykov, Yang Ning, Jun S. Liu, Han Liu

Our main theoretical contribution is to establish a unified Z-estimation theory of confidence regions for high dimensional problems.

Optimal linear estimation under unknown nonlinear transform

no code implementations NeurIPS 2015 Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu

This model is known as the single-index model in statistics, and, among other things, it represents a significant generalization of one-bit compressed sensing.

Graphical Fermat's Principle and Triangle-Free Graph Estimation

no code implementations23 Apr 2015 Junwei Lu, Han Liu

We consider the problem of estimating undirected triangle-free graphs of high dimensional distributions.

The Knowledge Gradient Policy Using A Sparse Additive Belief Model

no code implementations18 Mar 2015 Yan Li, Han Liu, Warren Powell

We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R\&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power.

Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model

no code implementations10 Mar 2015 Junwei Lu, Mladen Kolar, Han Liu

We develop a novel procedure for constructing confidence bands for components of a sparse additive model.

Additive models

Statistical Limits of Convex Relaxations

no code implementations4 Mar 2015 Zhaoran Wang, Quanquan Gu, Han Liu

Many high dimensional sparse learning problems are formulated as nonconvex optimization.

Sparse Learning Stochastic Block Model

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

no code implementations11 Feb 2015 Cheng Zhou, Fang Han, Xinsheng Zhang, Han Liu

Theoretically, we develop a theory for testing the equality of U-statistic based correlation matrices.

Local and Global Inference for High Dimensional Nonparanormal Graphical Models

no code implementations9 Feb 2015 Quanquan Gu, Yuan Cao, Yang Ning, Han Liu

Due to the presence of unknown marginal transformations, we propose a pseudo likelihood based inferential approach.

Provable Sparse Tensor Decomposition

no code implementations5 Feb 2015 Will Wei Sun, Junwei Lu, Han Liu, Guang Cheng

We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition components.

Click-Through Rate Prediction Tensor Decomposition +1

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

no code implementations30 Dec 2014 Tianqi Zhao, Mladen Kolar, Han Liu

Our de-biasing procedure does not require solving the $L_1$-penalized composite quantile regression.

On Semiparametric Exponential Family Graphical Models

no code implementations30 Dec 2014 Zhuoran Yang, Yang Ning, Han Liu

We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data.

Two-sample testing

A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models

no code implementations30 Dec 2014 Yang Ning, Han Liu

Specifically, we propose a decorrelated score function to handle the impact of high dimensional nuisance parameters.

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

no code implementations30 Dec 2014 Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu

We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models.

Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory

no code implementations23 Dec 2014 Tuo Zhao, Han Liu, Tong Zhang

This is the first result on the computational and statistical guarantees of the pathwise coordinate optimization framework in high dimensions.

Sparse Learning

Testing and Confidence Intervals for High Dimensional Proportional Hazards Model

no code implementations16 Dec 2014 Ethan X. Fang, Yang Ning, Han Liu

This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models.

Model Selection

A Likelihood Ratio Framework for High Dimensional Semiparametric Regression

no code implementations6 Dec 2014 Yang Ning, Tianqi Zhao, Han Liu

(i) We develop a regularized statistical chromatography approach to infer the parameter of interest under the proposed semiparametric generalized linear model without the need of estimating the unknown base measure function.

Selection bias

Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time

no code implementations NeurIPS 2014 Zhaoran Wang, Huanran Lu, Han Liu

In this paper, we propose a two-stage sparse PCA procedure that attains the optimal principal subspace estimator in polynomial time.

Accelerated Mini-batch Randomized Block Coordinate Descent Method

no code implementations NeurIPS 2014 Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu

When the regularization function is block separable, we can solve the minimization problems in a randomized block coordinate descent (RBCD) manner.

Sparse Learning Stochastic Optimization

Sparse PCA with Oracle Property

no code implementations NeurIPS 2014 Quanquan Gu, Zhaoran Wang, Han Liu

In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank-$k$, and attains a $\sqrt{s/n}$ statistical rate of convergence with $s$ being the subspace sparsity level and $n$ the sample size.

Multivariate Regression with Calibration

no code implementations NeurIPS 2014 Han Liu, Lie Wang, Tuo Zhao

We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models.

Activity Prediction

Mode Estimation for High Dimensional Discrete Tree Graphical Models

no code implementations NeurIPS 2014 Chao Chen, Han Liu, Dimitris Metaxas, Tianqi Zhao

Though the mode finding problem is generally intractable in high dimensions, this paper unveils that, if the distribution can be approximated well by a tree graphical model, mode characterization is significantly easier.

Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions

no code implementations14 Nov 2014 Mengdi Wang, Ethan X. Fang, Han Liu

For smooth convex problems, the SCGD can be accelerated to converge at a rate of $O(k^{-2/7})$ in the general case and $O(k^{-4/5})$ in the strongly convex case.

Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time

no code implementations22 Aug 2014 Zhaoran Wang, Huanran Lu, Han Liu

To optimally estimate sparse principal subspaces, we propose a two-stage computational framework named "tighten after relax": Within the 'relax' stage, we approximately solve a convex relaxation of sparse PCA with early stopping to obtain a desired initial estimator; For the 'tighten' stage, we propose a novel algorithm called sparse orthogonal iteration pursuit (SOAP), which iteratively refines the initial estimator by directly solving the underlying nonconvex problem.

High Dimensional Semiparametric Latent Graphical Model for Mixed Data

no code implementations29 Apr 2014 Jianqing Fan, Han Liu, Yang Ning, Hui Zou

Theoretically, the proposed methods achieve the same rates of convergence for both precision matrix estimation and eigenvector estimation, as if the latent variables were observed.

feature selection

High Dimensional Semiparametric Scale-Invariant Principal Component Analysis

no code implementations18 Feb 2014 Fang Han, Han Liu

We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA).

feature selection

Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning

no code implementations16 Jan 2014 Le Song, Han Liu, Ankur Parikh, Eric Xing

Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics.

Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification

no code implementations16 Dec 2013 Robert Vanderbei, Han Liu, Lie Wang, Kevin Lin

For the first approach, we note that the zero vector can be taken as the initial basic (infeasible) solution for the linear programming problem and therefore, if the true signal is very sparse, some variants of the simplex method can be expected to take only a small number of pivots to arrive at a solution.

Sparse Inverse Covariance Estimation with Calibration

no code implementations NeurIPS 2013 Tuo Zhao, Han Liu

We propose a semiparametric procedure for estimating high dimensional sparse inverse covariance matrix.

Model Selection

Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model

no code implementations NeurIPS 2013 Fang Han, Han Liu

In this paper we focus on the principal component regression and its application to high dimension non-Gaussian data.

Joint Estimation of Multiple Graphical Models from High Dimensional Time Series

no code implementations1 Nov 2013 Huitong Qiu, Fang Han, Han Liu, Brian Caffo

In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions.

Time Series

ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions

no code implementations14 Oct 2013 Fang Han, Han Liu

In the non-sparse setting, we show that ECA's performance is highly related to the effective rank of the covariance matrix.

Challenges of Big Data Analysis

no code implementations7 Aug 2013 Jianqing Fan, Fang Han, Han Liu

Big Data bring new opportunities to modern society and challenges to data scientists.

A Direct Estimation of High Dimensional Stationary Vector Autoregressions

no code implementations1 Jul 2013 Fang Han, Huanran Lu, Han Liu

In addition, we provide thorough experiments on both synthetic and real-world equity data to show that there are empirical advantages of our method over the lasso-type estimators in both parameter estimation and forecasting.

Time Series

Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models

no code implementations30 Jun 2013 Zhaoran Wang, Fang Han, Han Liu

We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension $d$ to scale with the series length $T$.

Time Series

Optimal Feature Selection in High-Dimensional Discriminant Analysis

no code implementations27 Jun 2013 Mladen Kolar, Han Liu

Through careful analysis, we establish rates of convergence that are significantly faster than the best known results and admit an optimal scaling of the sample size n, dimensionality p, and sparsity level s in the high-dimensional setting.

feature selection Variable Selection

Optimal computational and statistical rates of convergence for sparse nonconvex learning problems

no code implementations20 Jun 2013 Zhaoran Wang, Han Liu, Tong Zhang

In particular, our analysis improves upon existing results by providing a more refined sample complexity bound as well as an exact support recovery result for the final estimator.

Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution

no code implementations29 May 2013 Fang Han, Han Liu

The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery

no code implementations10 May 2013 Han Liu, Lie Wang, Tuo Zhao

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models.

Activity Prediction

Graph Estimation From Multi-attribute Data

no code implementations29 Oct 2012 Mladen Kolar, Han Liu, Eric P. Xing

Many real world network problems often concern multivariate nodal attributes such as image, textual, and multi-view feature vectors on nodes, rather than simple univariate nodal attributes.

Graph-Valued Regression

no code implementations NeurIPS 2010 Han Liu, Xi Chen, Larry Wasserman, John D. Lafferty

In this paper, we propose a semiparametric method for estimating $G(x)$ that builds a tree on the $X$ space just as in CART (classification and regression trees), but at each leaf of the tree estimates a graph.

Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

2 code implementations NeurIPS 2010 Han Liu, Kathryn Roeder, Larry Wasserman

In this paper, we present StARS: a new stability-based method for choosing the regularization parameter in high dimensional inference for undirected graphs.

Model Selection

Nonparametric regression and classification with joint sparsity constraints

no code implementations NeurIPS 2008 Han Liu, Larry Wasserman, John D. Lafferty

We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints.

Additive models Classification +1

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