Search Results for author: Han Liu

Found 203 papers, 55 papers with code

On Languaging a Simulation Engine

no code implementations26 Feb 2024 Han Liu, Liantang Li

Language model intelligence is revolutionizing the way we program materials simulations.

Language Modelling

AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods

no code implementations17 Feb 2024 Tim Tsz-Kit Lau, Han Liu, Mladen Kolar

The choice of batch sizes in stochastic gradient optimizers is critical for model training.

Image Classification

VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

no code implementations16 Feb 2024 Ziyi Yin, Muchao Ye, Tianrong Zhang, Jiaqi Wang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

Correspondingly, we propose a novel VQAttack model, which can iteratively generate both image and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module.

Adversarial Robustness Language Modelling +3

DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models

1 code implementation13 Feb 2024 Zhihan Zhou, Weimin Wu, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V Davuluri, Zhong Wang, Han Liu

To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer.

Contrastive Learning

On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis

no code implementations7 Feb 2024 Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song, Han Liu

Specifically, we establish an upper bound criterion for the norm of input query patterns and memory patterns.

Retrieval

HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text

1 code implementation NeurIPS 2023 Han Liu, Zhi Xu, Xiaotong Zhang, Feng Zhang, Fenglong Ma, Hongyang Chen, Hong Yu, Xianchao Zhang

Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.

Adversarial Attack Hard-label Attack +5

LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning

no code implementations29 Jan 2024 Yuqiang Sun, Daoyuan Wu, Yue Xue, Han Liu, Wei Ma, Lyuye Zhang, Miaolei Shi, Yang Liu

Large language models (LLMs) have demonstrated significant poten- tial for many downstream tasks, including those requiring human- level intelligence, such as vulnerability detection.

Vulnerability Detection

Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions

1 code implementation20 Jan 2024 Suhan Cui, Jiaqi Wang, Yuan Zhong, Han Liu, Ting Wang, Fenglong Ma

The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes has generated vast amounts of medical data, offering significant opportunities for improving healthcare services through deep learning techniques.

Neural Architecture Search

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.

STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction

no code implementations28 Dec 2023 Dennis Wu, Jerry Yao-Chieh Hu, Weijian Li, Bo-Yu Chen, Han Liu

We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series prediction with memory-enhanced capabilities.

Retrieval Time Series +1

Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation

1 code implementation21 Nov 2023 Han Liu, Yubo Fan, Zhoubing Xu, Benoit M. Dawant, Ipek Oguz

In this paper, we present our solution to tackle the multi-institutional unsupervised domain adaptation for the crossMoDA 2023 challenge.

Medical Image Segmentation Style Transfer +1

Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models

1 code implementation30 Oct 2023 Hao Li, Han Liu, Dewei Hu, Jiacheng Wang, Ipek Oguz

To address prevalent issues in medical imaging, such as data acquisition challenges and label availability, transfer learning from natural to medical image domains serves as a viable strategy to produce reliable segmentation results.

Image Segmentation Medical Image Segmentation +4

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

no code implementations29 Oct 2023 Han Liu, Xingshuo Huang, Xiaotong Zhang, Qimai Li, Fenglong Ma, Wei Wang, Hongyang Chen, Hong Yu, Xianchao Zhang

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction.

Adversarial Attack

VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

1 code implementation NeurIPS 2023 Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.

Adversarial Robustness

Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints

no code implementations28 Sep 2023 Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, Yuxin Chen

The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment.

On Sparse Modern Hopfield Model

1 code implementation NeurIPS 2023 Jerry Yao-Chieh Hu, Donglin Yang, Dennis Wu, Chenwei Xu, Bo-Yu Chen, Han Liu

Building upon this, we derive the sparse memory retrieval dynamics from the sparse energy function and show its one-step approximation is equivalent to the sparse-structured attention.

Retrieval

False Negative/Positive Control for SAM on Noisy Medical Images

1 code implementation20 Aug 2023 Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, Brett C Byram, Ipek Oguz

The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy.

Image Segmentation Medical Image Segmentation +2

CATS v2: Hybrid encoders for robust medical segmentation

2 code implementations11 Aug 2023 Hao Li, Han Liu, Dewei Hu, Xing Yao, Jiacheng Wang, Ipek Oguz

We fuse the information from the convolutional encoder and the transformer at the skip connections of different resolutions to form the final segmentation.

Domain Adaptation Image Segmentation +3

GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis

1 code implementation7 Aug 2023 Yuqiang Sun, Daoyuan Wu, Yue Xue, Han Liu, Haijun Wang, Zhengzi Xu, Xiaofei Xie, Yang Liu

Instead of relying solely on GPT to identify vulnerabilities, which can lead to high false positives and is limited by GPT's pre-trained knowledge, we utilize GPT as a versatile code understanding tool.

Vulnerability Detection

COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation

1 code implementation22 Jul 2023 Han Liu, Hao Li, Xing Yao, Yubo Fan, Dewei Hu, Benoit Dawant, Vishwesh Nath, Zhoubing Xu, Ipek Oguz

Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort.

Active Learning Image Segmentation +3

Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty

no code implementations15 Jul 2023 Guanlin Liu, Zhihan Zhou, Han Liu, Lifeng Lai

Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties.

reinforcement-learning Reinforcement Learning (RL)

Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints

no code implementations13 Jul 2023 Jaeyeon Jang, Diego Klabjan, Han Liu, Nital S. Patel, Xiuqi Li, Balakrishnan Ananthanarayanan, Husam Dauod, Tzung-Han Juang

This paper proposes a novel multi-agent reinforcement learning (MARL) method to learn multiple coordinated agents under directed acyclic graph (DAG) constraints.

Multi-agent Reinforcement Learning Scheduling

Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation

1 code implementation1 Jul 2023 Yongshang Li, Ronggui Ma, Han Liu, Gaoli Cheng

Deep learning plays an important role in crack segmentation, but most work utilize off-the-shelf or improved models that have not been specifically developed for this task.

Crack Segmentation Segmentation

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

3 code implementations26 Jun 2023 Zhihan Zhou, Yanrong Ji, Weijian Li, Pratik Dutta, Ramana Davuluri, Han Liu

Decoding the linguistic intricacies of the genome is a crucial problem in biology, and pre-trained foundational models such as DNABERT and Nucleotide Transformer have made significant strides in this area.

Computational Efficiency Core Promoter Detection +9

Feature Programming for Multivariate Time Series Prediction

1 code implementation9 Jun 2023 Alex Reneau, Jerry Yao-Chieh Hu, Chenwei Xu, Weijian Li, Ammar Gilani, Han Liu

We introduce the concept of programmable feature engineering for time series modeling and propose a feature programming framework.

Automated Feature Engineering Feature Engineering +3

Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms

1 code implementation25 May 2023 Tim Tsz-Kit Lau, Han Liu, Thomas Pock

We study the problem of approximate sampling from non-log-concave distributions, e. g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality.

Bayesian Inference Image Deconvolution

Boosting Few-Shot Text Classification via Distribution Estimation

no code implementations26 Mar 2023 Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Fenglong Ma, Xiao-Ming Wu, Hongyang Chen, Hong Yu, Xianchao Zhang

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain.

Few-Shot Image Classification Few-Shot Text Classification +2

Learning Human-Compatible Representations for Case-Based Decision Support

1 code implementation6 Mar 2023 Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan

Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans.

Classification Decision Making +1

Real-Time Image Demoireing on Mobile Devices

1 code implementation4 Feb 2023 Yuxin Zhang, Mingbao Lin, Xunchao Li, Han Liu, Guozhi Wang, Fei Chao, Shuai Ren, Yafei Wen, Xiaoxin Chen, Rongrong Ji

In this paper, we launch the first study on accelerating demoireing networks and propose a dynamic demoireing acceleration method (DDA) towards a real-time deployment on mobile devices.

HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation

1 code implementation13 Jan 2023 Han Liu, Yinwei Wei, Jianhua Yin, Liqiang Nie

Towards this end, existing methods tend to code users by modeling their Hamming similarities with the items they historically interact with, which are termed as the first-order similarities in this work.

Recommendation Systems

SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial Examples

1 code implementation CVPR 2023 Han Liu, Yuhao Wu, Zhiyuan Yu, Yevgeniy Vorobeychik, Ning Zhang

LiDAR-based perception is a central component of autonomous driving, playing a key role in tasks such as vehicle localization and obstacle detection.

Autonomous Driving

Biomedical image analysis competitions: The state of current participation practice

no code implementations16 Dec 2022 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Patrick Godau, Veronika Cheplygina, Michal Kozubek, Sharib Ali, Anubha Gupta, Jan Kybic, Alison Noble, Carlos Ortiz de Solórzano, Samiksha Pachade, Caroline Petitjean, Daniel Sage, Donglai Wei, Elizabeth Wilden, Deepak Alapatt, Vincent Andrearczyk, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Vivek Singh Bawa, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Jinwook Choi, Olivier Commowick, Marie Daum, Adrien Depeursinge, Reuben Dorent, Jan Egger, Hannah Eichhorn, Sandy Engelhardt, Melanie Ganz, Gabriel Girard, Lasse Hansen, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Hyunjeong Kim, Bennett Landman, Hongwei Bran Li, Jianning Li, Jun Ma, Anne Martel, Carlos Martín-Isla, Bjoern Menze, Chinedu Innocent Nwoye, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Carole Sudre, Kimberlin Van Wijnen, Armine Vardazaryan, Tom Vercauteren, Martin Wagner, Chuanbo Wang, Moi Hoon Yap, Zeyun Yu, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Rina Bao, Chanyeol Choi, Andrew Cohen, Oleh Dzyubachyk, Adrian Galdran, Tianyuan Gan, Tianqi Guo, Pradyumna Gupta, Mahmood Haithami, Edward Ho, Ikbeom Jang, Zhili Li, Zhengbo Luo, Filip Lux, Sokratis Makrogiannis, Dominik Müller, Young-tack Oh, Subeen Pang, Constantin Pape, Gorkem Polat, Charlotte Rosalie Reed, Kanghyun Ryu, Tim Scherr, Vajira Thambawita, Haoyu Wang, Xinliang Wang, Kele Xu, Hung Yeh, Doyeob Yeo, Yixuan Yuan, Yan Zeng, Xin Zhao, Julian Abbing, Jannes Adam, Nagesh Adluru, Niklas Agethen, Salman Ahmed, Yasmina Al Khalil, Mireia Alenyà, Esa Alhoniemi, Chengyang An, Talha Anwar, Tewodros Weldebirhan Arega, Netanell Avisdris, Dogu Baran Aydogan, Yingbin Bai, Maria Baldeon Calisto, Berke Doga Basaran, Marcel Beetz, Cheng Bian, Hao Bian, Kevin Blansit, Louise Bloch, Robert Bohnsack, Sara Bosticardo, Jack Breen, Mikael Brudfors, Raphael Brüngel, Mariano Cabezas, Alberto Cacciola, Zhiwei Chen, Yucong Chen, Daniel Tianming Chen, Minjeong Cho, Min-Kook Choi, Chuantao Xie Chuantao Xie, Dana Cobzas, Julien Cohen-Adad, Jorge Corral Acero, Sujit Kumar Das, Marcela de Oliveira, Hanqiu Deng, Guiming Dong, Lars Doorenbos, Cory Efird, Sergio Escalera, Di Fan, Mehdi Fatan Serj, Alexandre Fenneteau, Lucas Fidon, Patryk Filipiak, René Finzel, Nuno R. Freitas, Christoph M. Friedrich, Mitchell Fulton, Finn Gaida, Francesco Galati, Christoforos Galazis, Chang Hee Gan, Zheyao Gao, Shengbo Gao, Matej Gazda, Beerend Gerats, Neil Getty, Adam Gibicar, Ryan Gifford, Sajan Gohil, Maria Grammatikopoulou, Daniel Grzech, Orhun Güley, Timo Günnemann, Chunxu Guo, Sylvain Guy, Heonjin Ha, Luyi Han, Il Song Han, Ali Hatamizadeh, Tian He, Jimin Heo, Sebastian Hitziger, SeulGi Hong, Seungbum Hong, Rian Huang, Ziyan Huang, Markus Huellebrand, Stephan Huschauer, Mustaffa Hussain, Tomoo Inubushi, Ece Isik Polat, Mojtaba Jafaritadi, SeongHun Jeong, Bailiang Jian, Yuanhong Jiang, Zhifan Jiang, Yueming Jin, Smriti Joshi, Abdolrahim Kadkhodamohammadi, Reda Abdellah Kamraoui, Inha Kang, Junghwa Kang, Davood Karimi, April Khademi, Muhammad Irfan Khan, Suleiman A. Khan, Rishab Khantwal, Kwang-Ju Kim, Timothy Kline, Satoshi Kondo, Elina Kontio, Adrian Krenzer, Artem Kroviakov, Hugo Kuijf, Satyadwyoom Kumar, Francesco La Rosa, Abhi Lad, Doohee Lee, Minho Lee, Chiara Lena, Hao Li, Ling Li, Xingyu Li, Fuyuan Liao, Kuanlun Liao, Arlindo Limede Oliveira, Chaonan Lin, Shan Lin, Akis Linardos, Marius George Linguraru, Han Liu, Tao Liu, Di Liu, Yanling Liu, João Lourenço-Silva, Jingpei Lu, Jiangshan Lu, Imanol Luengo, Christina B. Lund, Huan Minh Luu, Yi Lv, Uzay Macar, Leon Maechler, Sina Mansour L., Kenji Marshall, Moona Mazher, Richard McKinley, Alfonso Medela, Felix Meissen, Mingyuan Meng, Dylan Miller, Seyed Hossein Mirjahanmardi, Arnab Mishra, Samir Mitha, Hassan Mohy-ud-Din, Tony Chi Wing Mok, Gowtham Krishnan Murugesan, Enamundram Naga Karthik, Sahil Nalawade, Jakub Nalepa, Mohamed Naser, Ramin Nateghi, Hammad Naveed, Quang-Minh Nguyen, Cuong Nguyen Quoc, Brennan Nichyporuk, Bruno Oliveira, David Owen, Jimut Bahan Pal, Junwen Pan, Wentao Pan, Winnie Pang, Bogyu Park, Vivek Pawar, Kamlesh Pawar, Michael Peven, Lena Philipp, Tomasz Pieciak, Szymon Plotka, Marcel Plutat, Fattaneh Pourakpour, Domen Preložnik, Kumaradevan Punithakumar, Abdul Qayyum, Sandro Queirós, Arman Rahmim, Salar Razavi, Jintao Ren, Mina Rezaei, Jonathan Adam Rico, ZunHyan Rieu, Markus Rink, Johannes Roth, Yusely Ruiz-Gonzalez, Numan Saeed, Anindo Saha, Mostafa Salem, Ricardo Sanchez-Matilla, Kurt Schilling, Wei Shao, Zhiqiang Shen, Ruize Shi, Pengcheng Shi, Daniel Sobotka, Théodore Soulier, Bella Specktor Fadida, Danail Stoyanov, Timothy Sum Hon Mun, Xiaowu Sun, Rong Tao, Franz Thaler, Antoine Théberge, Felix Thielke, Helena Torres, Kareem A. Wahid, Jiacheng Wang, Yifei Wang, Wei Wang, Xiong Wang, Jianhui Wen, Ning Wen, Marek Wodzinski, Ye Wu, Fangfang Xia, Tianqi Xiang, Chen Xiaofei, Lizhan Xu, Tingting Xue, Yuxuan Yang, Lin Yang, Kai Yao, Huifeng Yao, Amirsaeed Yazdani, Michael Yip, Hwanseung Yoo, Fereshteh Yousefirizi, Shunkai Yu, Lei Yu, Jonathan Zamora, Ramy Ashraf Zeineldin, Dewen Zeng, Jianpeng Zhang, Bokai Zhang, Jiapeng Zhang, Fan Zhang, Huahong Zhang, Zhongchen Zhao, Zixuan Zhao, Jiachen Zhao, Can Zhao, Qingshuo Zheng, Yuheng Zhi, Ziqi Zhou, Baosheng Zou, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

Of these, 84% were based on standard architectures.

Benchmarking

KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description

no code implementations30 Nov 2022 Chunyu Ma, Zhihan Zhou, Han Liu, David Koslicki

We believe it can effectively reduce "black-box" concerns and increase prediction confidence for drug repurposing based on predicted path-based explanations, and further accelerate the process of drug discovery for emerging diseases.

Drug Discovery

Drug repositioning for Alzheimer's disease with transfer learning

no code implementations27 Oct 2022 Yetao Wu, Han Liu, Jie Yan, Xiaolin Hu

After training, the model is used for virtual screening to find potential drugs for Alzheimer's disease (AD) treatment.

Drug Discovery Transfer Learning

Evaluation of Synthetically Generated CT for use in Transcranial Focused Ultrasound Procedures

1 code implementation26 Oct 2022 Han Liu, Michelle K. Sigona, Thomas J. Manuel, Li Min Chen, Benoit M. Dawant, Charles F. Caskey

Among 20 targets, differences in simulated peak pressure between rCT and sCT were largest without phase correction (12. 4$\pm$8. 1%) and smallest with Kranion phases (7. 3$\pm$6. 0%).

Generative Adversarial Network

Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation

1 code implementation16 Oct 2022 Ho Hin Lee, Yucheng Tang, Han Liu, Yubo Fan, Leon Y. Cai, Qi Yang, Xin Yu, Shunxing Bao, Yuankai Huo, Bennett A. Landman

We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets.

Computed Tomography (CT) Contrastive Learning +1

Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation

no code implementations23 Sep 2022 Han Liu, Yubo Fan, Ipek Oguz, Benoit M. Dawant

Automatic segmentation of vestibular schwannoma (VS) and cochlea from magnetic resonance imaging can facilitate VS treatment planning.

Segmentation Translation +1

Cats: Complementary CNN and Transformer Encoders for Segmentation

no code implementations24 Aug 2022 Hao Li, Dewei Hu, Han Liu, Jiacheng Wang, Ipek Oguz

We fuse the information from the convolutional encoder and the transformer, and pass it to the decoder to obtain the results.

3D Medical Imaging Segmentation Image Segmentation +1

A Real-time Fire Segmentation Method Based on A Deep Learning Approach

1 code implementation IFAC-PapersOnLine 2022 Mengna Li, Youmin Zhang, Lingxia Mu, Jing Xin, Ziquan Yu, Shangbin Jiao, Han Liu, Guo Xie, Yi Yingmin

Different from deeplabv3+, in order to improve the segmentation speed, this paper uses the lightweight network mobilenetv3 to build a new deep convolutional neural network and does not use atrous convolution, but it will affect the segmentation accuracy.

Real-Time Semantic Segmentation Segmentation

Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes

1 code implementation10 Jul 2022 Tim Tsz-Kit Lau, Han Liu

The proposed algorithms extend existing Langevin Monte Carlo algorithms in two aspects -- the ability to sample nonsmooth distributions with mirror descent-like algorithms, and the use of the more general Bregman--Moreau envelope in place of the Moreau envelope as a smooth approximation of the nonsmooth part of the potential.

Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection

no code implementations14 Jun 2022 Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Junjie Sun, Hong Yu, Xianchao Zhang

Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention.

Aspect Category Detection Contrastive Learning +2

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 +4

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 Reinforcement Learning (RL)

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).

Computational Efficiency Survival Prediction

ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities

1 code implementation7 Mar 2022 Han Liu, Yubo Fan, Hao Li, Jiacheng Wang, Dewei Hu, Can Cui, Ho Hin Lee, Huahong Zhang, Ipek Oguz

Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality.

Lesion Segmentation

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

1 code implementation21 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.

Generative Adversarial Network

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.

Segmentation 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.

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 Reinforcement Learning (RL)

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.

Segmentation 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 +3

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.

Attribute

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 Open-Ended Question Answering

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 Segmentation

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)

Document Classification General Classification +3

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.

regression Sparse Learning

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).

regression

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 Vocal Bursts Intensity Prediction

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 Speech Recognition +1

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 +3

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.

Clustering

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.

Attribute Clustering +3

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.

Attribute Graph Learning +2

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 Question Answering +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

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 +3

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.

Clustering Community Detection +1

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 Test

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.

Vocal Bursts Intensity Prediction

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 +1

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.

regression Test

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.

Binary Classification Cloud Detection +3

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.

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.

Vocal Bursts Intensity Prediction

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 regression +2

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

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

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.

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.

Clustering Retrieval

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

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.

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 Reinforcement Learning (RL)

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.

Clustering

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 +1

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

5 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 +1

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.

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.

valid

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.

Test Vocal Bursts Intensity Prediction

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.

valid

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 +3

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.

regression 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)}.

Retrieval

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.

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 $Y = (\boldsymbol{X}^{\top} \boldsymbol{\beta}^*)^2 + \varepsilon$.

Retrieval

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.

Test

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.

regression

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.

regression

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.