Search Results for author: Peilin Zhao

Found 105 papers, 38 papers with code

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

no code implementations23 Oct 2023 Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, Hao Wang

To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game.

ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking

no code implementations12 Oct 2023 Yiqiang Yi, Xu Wan, Yatao Bian, Le Ou-Yang, Peilin Zhao

Predicting the docking between proteins and ligands is a crucial and challenging task for drug discovery.

Drug Discovery Pose Prediction

Physics-Inspired Neural Graph ODE for Long-term Dynamical Simulation

no code implementations25 Aug 2023 Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li, Yu Rong

In PINGO, to ensure the uniqueness of the trajectory, we construct a Physics-Inspired Neural ODE framework to update the latent trajectory.

Differentiable Frank-Wolfe Optimization Layer

no code implementations21 Aug 2023 Zixuan Liu, Liu Liu, Xueqian Wang, Peilin Zhao

Differentiable optimization has received a significant amount of attention due to its foundational role in the domain of machine learning based on neural networks.

SR-R$^2$KAC: Improving Single Image Defocus Deblurring

no code implementations30 Jul 2023 Peng Tang, Zhiqiang Xu, Pengfei Wei, Xiaobin Hu, Peilin Zhao, Xin Cao, Chunlai Zhou, Tobias Lasser

To further alleviate the contingent effect of recursive stacking, i. e., ringing artifacts, we add identity shortcuts between atrous convolutions to simulate residual deconvolutions.

Deblurring Image Defocus Deblurring

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

no code implementations28 Jun 2023 Ziqiao Meng, Peilin Zhao, Yang Yu, Irwin King

Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery communities.

Drug Discovery Retrosynthesis

Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction

no code implementations5 Jun 2023 Ziqiao Meng, Peilin Zhao, Yang Yu, Irwin King

However, the current non-autoregressive decoder does not satisfy two essential rules of electron redistribution modeling simultaneously: the electron-counting rule and the symmetry rule.

Drug Discovery

Attention Paper: How Generative AI Reshapes Digital Shadow Industry?

no code implementations26 May 2023 Qichao Wang, Huan Ma, WenTao Wei, Hangyu Li, Liang Chen, Peilin Zhao, Binwen Zhao, Bo Hu, Shu Zhang, Zibin Zheng, Bingzhe Wu

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning.


SyNDock: N Rigid Protein Docking via Learnable Group Synchronization

no code implementations23 May 2023 Yuanfeng Ji, Yatao Bian, Guoji Fu, Peilin Zhao, Ping Luo

Firstly, SyNDock formulates multimeric protein docking as a problem of learning global transformations to holistically depict the placement of chain units of a complex, enabling a learning-centric solution.

Reweighted Mixup for Subpopulation Shift

no code implementations9 Apr 2023 Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, QinGhua Hu, Bingzhe Wu, Changqing Zhang, Jianhua Yao

Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions.

Fairness Generalization Bounds

Deploying Offline Reinforcement Learning with Human Feedback

no code implementations13 Mar 2023 Ziniu Li, Ke Xu, Liu Liu, Lanqing Li, Deheng Ye, Peilin Zhao

To address this issue, we propose an alternative framework that involves a human supervising the RL models and providing additional feedback in the online deployment phase.

Decision Making Model Selection +3

Towards Stable Test-Time Adaptation in Dynamic Wild World

1 code implementation24 Feb 2023 Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, Mingkui Tan

In this paper, we investigate the unstable reasons and find that the batch norm layer is a crucial factor hindering TTA stability.


Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed Recognition

1 code implementation CVPR 2023 Zhipeng Zhou, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Wei Gong

It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better.

Long-tail Learning

On the Pitfall of Mixup for Uncertainty Calibration

1 code implementation CVPR 2023 Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang

It has been recently found that models trained with mixup also perform well on uncertainty calibration.

Handling Missing Data via Max-Entropy Regularized Graph Autoencoder

no code implementations30 Nov 2022 Ziqi Gao, Yifan Niu, Jiashun Cheng, Jianheng Tang, Tingyang Xu, Peilin Zhao, Lanqing Li, Fugee Tsung, Jia Li

In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.


Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Network

no code implementations27 Oct 2022 Yiqiang Yi, Xu Wan, Kangfei Zhao, Le Ou-Yang, Peilin Zhao

The proposed ELGN firstly adds a super node to the 3D complex, and then builds a line graph based on the 3D complex.

Vertical Federated Linear Contextual Bandits

no code implementations20 Oct 2022 Zeyu Cao, Zhipeng Liang, Shu Zhang, Hangyu Li, Ouyang Wen, Yu Rong, Peilin Zhao, Bingzhe Wu

In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i. e., contextual information is vertically distributed over different departments.

Multi-Armed Bandits

Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation

no code implementations19 Oct 2022 Chengqian Gao, Ke Xu, Liu Liu, Deheng Ye, Peilin Zhao, Zhiqiang Xu

A promising paradigm for offline reinforcement learning (RL) is to constrain the learned policy to stay close to the dataset behaviors, known as policy constraint offline RL.

D4RL Offline RL +2

Pareto-aware Neural Architecture Generation for Diverse Computational Budgets

1 code implementation14 Oct 2022 Yong Guo, Yaofo Chen, Yin Zheng, Qi Chen, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan

More critically, these independent search processes cannot share their learned knowledge (i. e., the distribution of good architectures) with each other and thus often result in limited search results.

FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning

1 code implementation30 Sep 2022 Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, Dinghao Wu

Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.

Drug Discovery Multi-step retrosynthesis +2

MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction

no code implementations27 Sep 2022 Jiahan Liu, Chaochao Yan, Yang Yu, Chan Lu, Junzhou Huang, Le Ou-Yang, Peilin Zhao

In this paper, we propose a novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants.

Drug Discovery Graph Generation +1

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

1 code implementation19 Sep 2022 Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao

Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.

Generalization Bounds

Quantized Adaptive Subgradient Algorithms and Their Applications

no code implementations11 Aug 2022 Ke Xu, Jianqiao Wangni, Yifan Zhang, Deheng Ye, Jiaxiang Wu, Peilin Zhao

Therefore, a threshold quantization strategy with a relatively small error is adopted in QCMD adagrad and QRDA adagrad to improve the signal-to-noise ratio and preserve the sparsity of the model.


ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification

no code implementations23 May 2022 Liang Zeng, Lanqing Li, Ziqi Gao, Peilin Zhao, Jian Li

Motivated by this observation, we propose a principled GCL framework on Imbalanced node classification (ImGCL), which automatically and adaptively balances the representations learned from GCL without labels.

Classification Contrastive Learning +2

GPN: A Joint Structural Learning Framework for Graph Neural Networks

no code implementations12 May 2022 Qianggang Ding, Deheng Ye, Tingyang Xu, Peilin Zhao

To the best of our knowledge, our method is the first GNN-based bilevel optimization framework for resolving this task.

Bilevel Optimization

Efficient Test-Time Model Adaptation without Forgetting

1 code implementation6 Apr 2022 Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, Mingkui Tan

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w. r. t.


Boost Test-Time Performance with Closed-Loop Inference

no code implementations21 Mar 2022 Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Guanghui Xu, Haokun Li, Peilin Zhao, Junzhou Huang, YaoWei Wang, Mingkui Tan

Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance.

Auxiliary Learning Test

Learning Neural Set Functions Under the Optimal Subset Oracle

1 code implementation3 Mar 2022 Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian

Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery.

Anomaly Detection Drug Discovery +2

Transformer for Graphs: An Overview from Architecture Perspective

1 code implementation17 Feb 2022 Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, Yu Rong

In this survey, we provide a comprehensive review of various Graph Transformer models from the architectural design perspective.

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

no code implementations25 Jan 2022 Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Junzhou Huang, Da Luo, Kangyi Lin, Sophia Ananiadou

Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests.

Click-Through Rate Prediction Representation Learning

RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction

1 code implementation20 Dec 2021 Chaochao Yan, Peilin Zhao, Chan Lu, Yang Yu, Junzhou Huang

To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates.

Retrosynthesis Single-step retrosynthesis

SVIP: Sequence VerIfication for Procedures in Videos

1 code implementation CVPR 2022 Yicheng Qian, Weixin Luo, Dongze Lian, Xu Tang, Peilin Zhao, Shenghua Gao

In this paper, we propose a novel sequence verification task that aims to distinguish positive video pairs performing the same action sequence from negative ones with step-level transformations but still conducting the same task.

Action Detection Action Recognition

Graph Convolutional Module for Temporal Action Localization in Videos

no code implementations1 Dec 2021 Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan

To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms.

Ranked #2 on Temporal Action Localization on THUMOS’14 (mAP IOU@0.1 metric)

Action Recognition Temporal Action Localization

$p$-Laplacian Based Graph Neural Networks

1 code implementation14 Nov 2021 Guoji Fu, Peilin Zhao, Yatao Bian

Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and topological information simultaneously.

Node Classification

Meta-learning with an Adaptive Task Scheduler

2 code implementations NeurIPS 2021 Huaxiu Yao, Yu Wang, Ying WEI, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn

In ATS, for the first time, we design a neural scheduler to decide which meta-training tasks to use next by predicting the probability being sampled for each candidate task, and train the scheduler to optimize the generalization capacity of the meta-model to unseen tasks.

Drug Discovery Meta-Learning

Value Penalized Q-Learning for Recommender Systems

no code implementations15 Oct 2021 Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan, Peilin Zhao

To alleviate the action distribution shift problem in extracting RL policy from static trajectories, we propose Value Penalized Q-learning (VPQ), an uncertainty-based offline RL algorithm.

Offline RL Q-Learning +2

Weakly Supervised Graph Clustering

no code implementations29 Sep 2021 Tian Bian, Tingyang Xu, Yu Rong, Wenbing Huang, Xi Xiao, Peilin Zhao, Junzhou Huang, Hong Cheng

Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.

Clustering Graph Clustering +1

Local Augmentation for Graph Neural Networks

1 code implementation8 Sep 2021 Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu

To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.

Open-Ended Question Answering

AdaXpert: Adapting Neural Architecture for Growing Data

1 code implementation1 Jul 2021 Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu, Yifan Zhang, Yong Guo, Peilin Zhao, Peng Wang, Mingkui Tan

To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data.

Learning Graphon Autoencoders for Generative Graph Modeling

no code implementations29 May 2021 Hongteng Xu, Peilin Zhao, Junzhou Huang, Dixin Luo

A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons (and the corresponding observed graphs).

Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation

1 code implementation ICCV 2021 Jinyu Yang, Chunyuan Li, Weizhi An, Hehuan Ma, Yuzhi Guo, Yu Rong, Peilin Zhao, Junzhou Huang

Recent studies imply that deep neural networks are vulnerable to adversarial examples -- inputs with a slight but intentional perturbation are incorrectly classified by the network.

Segmentation Semantic Segmentation +1

Sparse online relative similarity learning

no code implementations15 Apr 2021 Dezhong Yao, Peilin Zhao, Chen Yu, Hai Jin, Bin Li

This is clearly inefficient for high dimensional tasks due to its high memory and computational complexity.

Metric Learning

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Molecular Property Prediction

Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets

no code implementations27 Feb 2021 Yong Guo, Yaofo Chen, Yin Zheng, Qi Chen, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan

To this end, we propose a Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an arbitrary budget as input and produces the Pareto optimal architecture for the target budget.

Towards Accurate and Compact Architectures via Neural Architecture Transformer

2 code implementations20 Feb 2021 Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Zhipeng Li, Jian Chen, Peilin Zhao, Junzhou Huang

To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization.

Neural Architecture Search valid

Pareto-Frontier-aware Neural Architecture Search

no code implementations1 Jan 2021 Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan

To find promising architectures under different budgets, existing methods may have to perform an independent search for each budget, which is very inefficient and unnecessary.

Neural Architecture Search

Hierarchical Graph Capsule Network

1 code implementation16 Dec 2020 Jinyu Yang, Peilin Zhao, Yu Rong, Chaochao Yan, Chunyuan Li, Hehuan Ma, Junzhou Huang

Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data.

Graph Classification

Adversarial Sparse Transformer for Time Series Forecasting

1 code implementation NeurIPS 2020 Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying WEI, Junzhou Huang

Specifically, AST adopts a Sparse Transformer as the generator to learn a sparse attention map for time series forecasting, and uses a discriminator to improve the prediction performance from sequence level.

Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +1

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

no code implementations25 Nov 2020 Deheng Ye, Guibin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang

Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.

Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?

no code implementations12 Jul 2020 Tian Bian, Xi Xiao, Tingyang Xu, Yu Rong, Wenbing Huang, Peilin Zhao, Junzhou Huang

Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs.

Clustering Community Detection +3

Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

1 code implementation ICML 2020 Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan

With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method significantly improves the search efficiency and finds better architectures than existing NAS methods.

Neural Architecture Search

Relation-Aware Transformer for Portfolio Policy Learning

2 code implementations IJCAI 2020 Ke Xu, Yifan Zhang, Deheng Ye, Peilin Zhao, Mingkui Tan

One of the key issues is how to represent the non-stationary price series of assets in a portfolio, which is important for portfolio decisions.

Towards Fast Adaptation of Neural Architectures with Meta Learning

1 code implementation ICLR 2020 Dongze Lian, Yin Zheng, Yintao Xu, Yanxiong Lu, Leyu Lin, Peilin Zhao, Junzhou Huang, Shenghua Gao

Recently, Neural Architecture Search (NAS) has been successfully applied to multiple artificial intelligence areas and shows better performance compared with hand-designed networks.

Few-Shot Learning Neural Architecture Search

COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19

1 code implementation30 Apr 2020 Yifan Zhang, Shuaicheng Niu, Zhen Qiu, Ying WEI, Peilin Zhao, Jianhua Yao, Junzhou Huang, Qingyao Wu, Mingkui Tan

There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.

COVID-19 Diagnosis Domain Adaptation

Disturbance-immune Weight Sharing for Neural Architecture Search

no code implementations29 Mar 2020 Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yong Guo, Peilin Zhao, Junzhou Huang, Mingkui Tan

To alleviate the performance disturbance issue, we propose a new disturbance-immune update strategy for model updating.

Neural Architecture Search

Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

no code implementations12 Mar 2020 Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li

However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices.

Privacy Preserving

Context-Aware Domain Adaptation in Semantic Segmentation

no code implementations9 Mar 2020 Jinyu Yang, Weizhi An, Chaochao Yan, Peilin Zhao, Junzhou Huang

To achieve this goal, we design two cross-domain attention modules to adapt context dependencies from both spatial and channel views.

Semantic Segmentation Unsupervised Domain Adaptation

Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning

no code implementations6 Mar 2020 Yifan Zhang, Peilin Zhao, Qingyao Wu, Bin Li, Junzhou Huang, Mingkui Tan

This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs.

reinforcement-learning Reinforcement Learning (RL)

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

2 code implementations17 Jan 2020 Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang

Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge.

Online Adaptive Asymmetric Active Learning with Limited Budgets

1 code implementation18 Nov 2019 Yifan Zhang, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, JieZhang Cao, Junzhou Huang, Mingkui Tan

In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced.

Active Learning Anomaly Detection

Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis

1 code implementation17 Nov 2019 Yifan Zhang, Ying WEI, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, Mingkui Tan, Junzhou Huang

In this paper, we seek to exploit rich labeled data from relevant domains to help the learning in the target task with unsupervised domain adaptation (UDA).

Unsupervised Domain Adaptation

NAT: Neural Architecture Transformer for Accurate and Compact Architectures

1 code implementation NeurIPS 2019 Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Jian Chen, Peilin Zhao, Junzhou Huang

To verify the effectiveness of the proposed strategies, we apply NAT on both hand-crafted architectures and NAS based architectures.

Neural Architecture Search

Aggregated Gradient Langevin Dynamics

no code implementations21 Oct 2019 Chao Zhang, Jiahao Xie, Zebang Shen, Peilin Zhao, Tengfei Zhou, Hui Qian

In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling.

Towards Interpreting Deep Neural Networks via Understanding Layer Behaviors

no code implementations25 Sep 2019 JieZhang Cao, Jincheng Li, Xiping Hu, Peilin Zhao, Mingkui Tan

ii) the $W$-distance of a specific layer to the target distribution tends to decrease along training iterations.

Transferable Neural Processes for Hyperparameter Optimization

no code implementations7 Sep 2019 Ying Wei, Peilin Zhao, Huaxiu Yao, Junzhou Huang

Automated machine learning aims to automate the whole process of machine learning, including model configuration.

BIG-bench Machine Learning Hyperparameter Optimization +1

Graph Convolutional Networks for Temporal Action Localization

1 code implementation ICCV 2019 Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan

Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.

Ranked #4 on Temporal Action Localization on THUMOS’14 (mAP IOU@0.1 metric)

Action Classification Temporal Action Localization

Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend

no code implementations29 Jan 2019 Yawei Zhao, Chen Yu, Peilin Zhao, Hanlin Tang, Shuang Qiu, Ji Liu

Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers.

Hyperparameter Learning via Distributional Transfer

1 code implementation NeurIPS 2019 Ho Chung Leon Law, Peilin Zhao, Lucian Chan, Junzhou Huang, Dino Sejdinovic

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved.

Bayesian Optimisation

Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching

no code implementations27 Sep 2018 JieZhang Cao, Yong Guo, Langyuan Mo, Peilin Zhao, Junzhou Huang, Mingkui Tan

We study the joint distribution matching problem which aims at learning bidirectional mappings to match the joint distribution of two domains.

Open-Ended Question Answering Unsupervised Image-To-Image Translation +2

Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss

no code implementations19 Sep 2018 Yong Guo, Qi Chen, Jian Chen, Junzhou Huang, Yanwu Xu, JieZhang Cao, Peilin Zhao, Mingkui Tan

However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance.

Image Super-Resolution

A Boosting Framework of Factorization Machine

no code implementations17 Apr 2018 Longfei Li, Peilin Zhao, Jun Zhou, Xiaolong Li

However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets.

Recommendation Systems

Distributed Collaborative Hashing and Its Applications in Ant Financial

no code implementations13 Apr 2018 Chaochao Chen, Ziqi Liu, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li

The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure.

Collaborative Filtering

Adaptive Cost-sensitive Online Classification

no code implementations6 Apr 2018 Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost.

Anomaly Detection Classification +2

Online Learning: A Comprehensive Survey

no code implementations8 Feb 2018 Steven C. H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao

Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a time.

BIG-bench Machine Learning Decision Making

Online Compact Convexified Factorization Machine

no code implementations5 Feb 2018 Wenpeng Zhang, Xiao Lin, Peilin Zhao

To address this subsequent challenge, we follow the general projection-free algorithmic framework of Online Conditional Gradient and propose an Online Compact Convex Factorization Machine (OCCFM) algorithm that eschews the projection operation with efficient linear optimization steps.

Binary Classification Feature Engineering

Projection-free Distributed Online Learning in Networks

no code implementations ICML 2017 Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang

The conditional gradient algorithm has regained a surge of research interest in recent years due to its high efficiency in handling large-scale machine learning problems.

Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback

no code implementations3 Jul 2017 Peng Yang, Peilin Zhao, Xin Gao, Yong liu

Morever, the proposed algorithm can be scaled up to large-sized datasets after a relaxation.

Robust Online Multi-Task Learning with Correlative and Personalized Structures

no code implementations6 Jun 2017 Peng Yang, Peilin Zhao, Xin Gao

Multi-Task Learning (MTL) can enhance a classifier's generalization performance by learning multiple related tasks simultaneously.

Multi-Task Learning

Online Bayesian Collaborative Topic Regression

no code implementations28 May 2016 Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun

In this paper, we propose a novel scheme of Online Bayesian Collaborative Topic Regression (OBCTR) which is efficient and scalable for learning from data streams.

Recommendation Systems regression

Adaptive Subgradient Methods for Online AUC Maximization

no code implementations1 Feb 2016 Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong

Despite their encouraging results reported, the existing online AUC maximization algorithms often adopt simple online gradient descent approaches that fail to exploit the geometrical knowledge of the data observed during the online learning process, and thus could suffer from relatively larger regret.

Budget Online Multiple Kernel Learning

no code implementations16 Nov 2015 Jing Lu, Steven C. H. Hoi, Doyen Sahoo, Peilin Zhao

To overcome this drawback, we present a novel framework of Budget Online Multiple Kernel Learning (BOMKL) and propose a new Sparse Passive Aggressive learning to perform effective budget online learning.

General Classification

A Framework of Sparse Online Learning and Its Applications

no code implementations25 Jul 2015 Dayong Wang, Pengcheng Wu, Peilin Zhao, Steven C. H. Hoi

Unlike some existing online data stream classification techniques that are often based on first-order online learning, we propose a framework of Sparse Online Classification (SOC) for data stream classification, which includes some state-of-the-art first-order sparse online learning algorithms as special cases and allows us to derive a new effective second-order online learning algorithm for data stream classification.

Anomaly Detection Classification +1

Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling

no code implementations13 May 2014 Peilin Zhao, Tong Zhang

Stochastic Gradient Descent (SGD) is a popular optimization method which has been applied to many important machine learning tasks such as Support Vector Machines and Deep Neural Networks.

Stochastic Optimization with Importance Sampling

no code implementations13 Jan 2014 Peilin Zhao, Tong Zhang

Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA).

Stochastic Optimization

Adaptive Stochastic Alternating Direction Method of Multipliers

no code implementations16 Dec 2013 Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li

The Alternating Direction Method of Multipliers (ADMM) has been studied for years.

Active Learning with Expert Advice

no code implementations26 Sep 2013 Peilin Zhao, Steven Hoi, Jinfeng Zhuang

In this paper, we address a new problem of active learning with expert advice, where the outcome of an instance is disclosed only when it is requested by the online learner.

Active Learning

PAMR: Passive aggressive mean reversion strategy for portfolio selection

1 code implementation Machine Learning 2012 Bin Li, Peilin Zhao, Steven C. H. Hoi

This article proposes a novel online portfolio selection strategy named “Passive Aggressive Mean Reversion” (PAMR).

DUOL: A Double Updating Approach for Online Learning

no code implementations NeurIPS 2009 Peilin Zhao, Steven C. Hoi, Rong Jin

This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors.

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