Search Results for author: Ke Tang

Found 58 papers, 12 papers with code

Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles

1 code implementation23 Nov 2022 Shengcai Liu, Fu Peng, Ke Tang

Attack Ensemble (AE), which combines multiple attacks together, provides a reliable way to evaluate adversarial robustness.

Adversarial Robustness

Causality-driven Hierarchical Structure Discovery for Reinforcement Learning

no code implementations13 Oct 2022 Shaohui Peng, Xing Hu, Rui Zhang, Ke Tang, Jiaming Guo, Qi Yi, Ruizhi Chen, Xishan Zhang, Zidong Du, Ling Li, Qi Guo, Yunji Chen

To address this issue, we propose CDHRL, a causality-driven hierarchical reinforcement learning framework, leveraging a causality-driven discovery instead of a randomness-driven exploration to effectively build high-quality hierarchical structures in complicated environments.

Hierarchical Reinforcement Learning reinforcement-learning

How Good Is Neural Combinatorial Optimization?

1 code implementation22 Sep 2022 Shengcai Liu, Yu Zhang, Ke Tang, Xin Yao

Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts.

Combinatorial Optimization Traveling Salesman Problem

Disentangled Contrastive Learning for Social Recommendation

no code implementations18 Aug 2022 Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qing Li, Ke Tang

In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.

Contrastive Learning Representation Learning +1

GANDSE: Generative Adversarial Network based Design Space Exploration for Neural Network Accelerator Design

no code implementations1 Aug 2022 Lang Feng, Wenjian Liu, Chuliang Guo, Ke Tang, Cheng Zhuo, Zhongfeng Wang

To improve the design quality while saving the cost, design automation for neural network accelerators was proposed, where design space exploration algorithms are used to automatically search the optimized accelerator design within a design space.

Defending Adversarial Examples by Negative Correlation Ensemble

1 code implementation11 Jun 2022 Wenjian Luo, Hongwei Zhang, Linghao Kong, Zhijian Chen, Ke Tang

The security issues in DNNs, such as adversarial examples, have attracted much attention.

Adversarial Robustness

Saliency Attack: Towards Imperceptible Black-box Adversarial Attack

1 code implementation4 Jun 2022 Zeyu Dai, Shengcai Liu, Ke Tang, Qing Li

In this paper, we propose to restrict the perturbations to a small salient region to generate adversarial examples that can hardly be perceived.

Adversarial Attack

Training Quantized Deep Neural Networks via Cooperative Coevolution

1 code implementation23 Dec 2021 Fu Peng, Shengcai Liu, Ning Lu, Ke Tang

This work considers a challenging Deep Neural Network(DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations.


HydraText: Multi-objective Optimization for Adversarial Textual Attack

no code implementations2 Nov 2021 Shengcai Liu, Ning Lu, Wenjing Hong, Chao Qian, Ke Tang

The field of adversarial textual attack has significantly grown over the last few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model.

Semantic Similarity Semantic Textual Similarity

Imperceptible Black-box Attack via Refining in Salient Region

no code implementations29 Sep 2021 Zeyu Dai, Shengcai Liu, Ke Tang, Qing Li

To address this issue, in this paper we propose to use segmentation priors for black-box attacks such that the perturbations are limited in the salient region.

An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States

no code implementations21 Sep 2021 Lin William Cong, Ke Tang, Bing Wang, Jingyuan Wang

We build a deep-learning-based SEIR-AIM model integrating the classical Susceptible-Exposed-Infectious-Removed epidemiology model with forecast modules of infection, community mobility, and unemployment.


Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack

no code implementations6 Sep 2021 Shengcai Liu, Ning Lu, Cheng Chen, Ke Tang

Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing.

Combinatorial Optimization

Crypto Wash Trading

no code implementations24 Aug 2021 Lin William Cong, Xi Li, Ke Tang, Yang Yang

We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges.

Deep Sequence Modeling: Development and Applications in Asset Pricing

no code implementations20 Aug 2021 Lin William Cong, Ke Tang, Jingyuan Wang, Yang Zhang

We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling.

Time Series

Active Reinforcement Learning over MDPs

no code implementations5 Aug 2021 Qi Yang, Peng Yang, Ke Tang

This paper proposes a framework of Active Reinforcement Learning (ARL) over MDPs to improve generalization efficiency in a limited resource by instance selection.


Multi-Domain Active Learning: Literature Review and Comparative Study

1 code implementation25 Jun 2021 Rui He, Shengcai Liu, Shan He, Ke Tang

Active learning (AL) can be utilized in MDL to reduce the labeling effort by only using the most informative data.

Active Learning

Robust Dynamic Network Embedding via Ensembles

3 code implementations30 May 2021 Chengbin Hou, Guoji Fu, Peng Yang, Zheng Hu, Shan He, Ke Tang

It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.

Network Embedding

A New Knowledge Gradient-based Method for Constrained Bayesian Optimization

no code implementations20 Jan 2021 Wenjie Chen, Shengcai Liu, Ke Tang

An unbiased estimator of the gradient of the new acquisition function is derived to implement the $c-\rm{KG}$ approach.

A Survey on Neural Network Interpretability

no code implementations28 Dec 2020 Yu Zhang, Peter Tiňo, Aleš Leonardis, Ke Tang

Along with the great success of deep neural networks, there is also growing concern about their black-box nature.

Drug Discovery

Memetic Search for Vehicle Routing with Simultaneous Pickup-Delivery and Time Windows

1 code implementation12 Nov 2020 Shengcai Liu, Ke Tang, Xin Yao

The Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW) has attracted much research interest in the last decade, due to its wide application in modern logistics.

Interpreting Deep Learning Model Using Rule-based Method

no code implementations15 Oct 2020 Xiaojian Wang, Jingyuan Wang, Ke Tang

For global explanation, frequency-based and out-of-bag based methods are proposed to extract important features in the neural network decision.

Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search

no code implementations8 Sep 2020 Hu Zhang, Peng Yang, Yanglong Yu, Mingjia Li, Ke Tang

Evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability.

Atari Games reinforcement-learning

GloDyNE: Global Topology Preserving Dynamic Network Embedding

2 code implementations5 Aug 2020 Chengbin Hou, Han Zhang, Shan He, Ke Tang

The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step.

Graph Reconstruction Incremental Learning +1

Few-shots Parallel Algorithm Portfolio Construction via Co-evolution

no code implementations1 Jul 2020 Ke Tang, Shengcai Liu, Peng Yang, Xin Yao

In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of training problem instances, which is often referred to as PAP construction.

Traveling Salesman Problem

Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States

no code implementations9 Mar 2020 Jingyuan Wang, Ke Tang, Kai Feng, Xin Li, Weifeng Lv, Kun Chen, Fei Wang

Primary outcome measures: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).


Optimal Stochastic and Online Learning with Individual Iterates

no code implementations NeurIPS 2019 Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou

In this paper, we propose a theoretically sound strategy to select an individual iterate of the vanilla SCMD, which is able to achieve optimal rates for both convex and strongly convex problems in a non-smooth learning setting.

online learning Sparse Learning

Explicit Planning for Efficient Exploration in Reinforcement Learning

no code implementations NeurIPS 2019 Liangpeng Zhang, Ke Tang, Xin Yao

We argue that explicit planning for exploration can help alleviate such a problem, and propose a Value Iteration for Exploration Cost (VIEC) algorithm which computes the optimal exploration scheme by solving an augmented MDP.

Efficient Exploration reinforcement-learning

On Performance Estimation in Automatic Algorithm Configuration

no code implementations19 Nov 2019 Shengcai Liu, Ke Tang, Yunwen Lei, Xin Yao

Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress.

Parallel Exploration via Negatively Correlated Search

no code implementations16 Oct 2019 Peng Yang, Qi Yang, Ke Tang, Xin Yao

Empirical results show that the significant advantages of NCS over the compared state-of-the-art methods can be highly owed to the effective parallel exploration ability.

Atari Games reinforcement-learning

Competitive Coevolution as an Adversarial Approach to Dynamic Optimization

no code implementations31 Jul 2019 Xiaofen Lu, Ke Tang, Stefan Menzel, Xin Yao

In this paper, a new framework of employing EAs in the context of dynamic optimization is explored.

On the Robustness of Median Sampling in Noisy Evolutionary Optimization

no code implementations28 Jul 2019 Chao Bian, Chao Qian, Yang Yu, Ke Tang

Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value.

DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding

2 code implementations arXiv 2019 Chengbin Hou, Han Zhang, Ke Tang, Shan He

Dynamic network embedding aims to learn low dimensional embeddings for unseen and seen nodes by using any currently available snapshots of a dynamic network.

Graph Reconstruction Link Prediction +1

AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks

no code implementations24 Jul 2019 Jingyuan Wang, Yang Zhang, Ke Tang, Junjie Wu, Zhang Xiong

Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT).

Deep Attention reinforcement-learning +1

Running Time Analysis of the (1+1)-EA for Robust Linear Optimization

no code implementations17 Jun 2019 Chao Bian, Chao Qian, Ke Tang

Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties.

Decision Making with Machine Learning and ROC Curves

no code implementations5 May 2019 Kai Feng, Han Hong, Ke Tang, Jingyuan Wang

Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.

BIG-bench Machine Learning Decision Making +2

Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions

no code implementations3 Feb 2019 Yunwen Lei, Ting Hu, Guiying Li, Ke Tang

While the behavior of SGD is well understood in the convex learning setting, the existing theoretical results for SGD applied to nonconvex objective functions are far from mature.

A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization

no code implementations6 Dec 2018 Peng Yang, Ke Tang, Xin Yao

Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas.

Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities

no code implementations NeurIPS 2018 Yunwen Lei, Ke Tang

We apply the derived computational error bounds to study the generalization performance of multi-pass stochastic gradient descent (SGD) in a non-parametric setting.

Generalization Bounds

Attributed Network Embedding for Incomplete Attributed Networks

1 code implementation28 Nov 2018 Chengbin Hou, Shan He, Ke Tang

Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e. g. a social network with user profiles.

Link Prediction Network Embedding +1

Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization

no code implementations16 Oct 2018 Yibo Zhang, Chao Qian, Ke Tang

Under a convex polytope constraint, we prove that LDGM can achieve a $(1-e^{-\beta}-\epsilon)$-approximation guarantee after $O(1/\epsilon)$ iterations, which is the same as the best previous gradient-based algorithm.

Analysis of Noisy Evolutionary Optimization When Sampling Fails

no code implementations11 Oct 2018 Chao Qian, Chao Bian, Yang Yu, Ke Tang, Xin Yao

In noisy evolutionary optimization, sampling is a common strategy to deal with noise.

Automatic Construction of Parallel Portfolios via Explicit Instance Grouping

no code implementations17 Apr 2018 Shengcai Liu, Ke Tang, Xin Yao

Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems.

Subset Selection under Noise

no code implementations NeurIPS 2017 Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou

The problem of selecting the best $k$-element subset from a universe is involved in many applications.

Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms

no code implementations20 Nov 2017 Chao Qian, Yang Yu, Ke Tang, Xin Yao, Zhi-Hua Zhou

To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems.

Combinatorial Optimization

Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise

no code implementations2 Nov 2017 Chao Qian, Chao Bian, Wu Jiang, Ke Tang

We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds.

Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization

no code implementations3 Aug 2017 Jinyuan Zhang, Aimin Zhou, Ke Tang, Guixu Zhang

Finally it uses the classifier to filter the unpromising candidate offspring solutions and choose a promising one from the generated candidate offspring set for each parent solution.

Classification General Classification +1

Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results

no code implementations12 Jun 2017 Bingshui Da, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Zexuan Zhu, Chuan-Kang Ting, Ke Tang, Xin Yao

In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective optimization tasks that need to be solved simultaneously.

Experience-based Optimization: A Coevolutionary Approach

no code implementations29 Mar 2017 Shengcai Liu, Ke Tang, Xin Yao

The idea behind LiangYi is to promote the population-based solver by training it (with the training module) to improve its performance on those instances (discovered by the sampling module) on which it performs badly, while keeping the good performances obtained by it on previous instances.

An Adaptive Framework to Tune the Coordinate Systems in Evolutionary Algorithms

no code implementations18 Mar 2017 Zhi-Zhong Liu, Yong Wang, Shengxiang Yang, Ke Tang

In the evolutionary computation research community, the performance of most evolutionary algorithms (EAs) depends strongly on their implemented coordinate system.

Concept Drift Adaptation by Exploiting Historical Knowledge

no code implementations12 Feb 2017 Yu Sun, Ke Tang, Zexuan Zhu, Xin Yao

Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be re-trained to attain new models for the current data.

Ensemble Learning Incremental Learning +1

Success Probability of Exploration: a Concrete Analysis of Learning Efficiency

no code implementations2 Dec 2016 Liangpeng Zhang, Ke Tang, Xin Yao

We then provide empirical results to verify our approach, and demonstrate how the success probability of exploration can be used to analyse and predict the behaviours and possible outcomes of exploration, which are the keys to the answer of the important questions of exploration.

High-dimensional Black-box Optimization via Divide and Approximate Conquer

no code implementations11 Mar 2016 Peng Yang, Ke Tang, Xin Yao

Divide and Conquer (DC) is conceptually well suited to high-dimensional optimization by decomposing a problem into multiple small-scale sub-problems.

Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection

1 code implementation25 Jan 2016 Guiying Li, Junlong Liu, Chunhui Jiang, Liangpeng Zhang, Minlong Lin, Ke Tang

R-CNN style methods are sorts of the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification.

object-detection Real-Time Object Detection +1

Negatively Correlated Search

no code implementations20 Apr 2015 Ke Tang, Peng Yang, Xin Yao

This paper presents a new EA, namely Negatively Correlated Search (NCS), which maintains multiple individual search processes in parallel and models the search behaviors of individual search processes as probability distributions.

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