Zhi-Hua Zhou

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Nanjing University

Professor, Department of Computer Science & Technology

Exploratory Machine Learning with Unknown Unknowns

In conventional supervised learning, a training dataset is given with gr...
02/05/2020 ∙ by Yu-Jie Zhang, et al. ∙ 72 ∙ share

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An Unbiased Risk Estimator for Learning with Augmented Classes

In this paper, we study the problem of learning with augmented classes (...
10/21/2019 ∙ by Yu-Jie Zhang, et al. ∙ 31 ∙ share

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Prediction with Unpredictable Feature Evolution

Feature space can change or evolve when learning with streaming data. Se...
04/27/2019 ∙ by Bo-Jian Hou, et al. ∙ 30 ∙ share

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Model Reuse with Reduced Kernel Mean Embedding Specification

Given a publicly available pool of machine learning models constructed f...
01/20/2020 ∙ by Xi-Zhu Wu, et al. ∙ 26 ∙ share

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Multi-Label Learning with Deep Forest

In multi-label learning, each instance is associated with multiple label...
11/15/2019 ∙ by Liang Yang, et al. ∙ 22 ∙ share

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Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

In this work, we consider one challenging training time attack by modify...
05/22/2019 ∙ by Ji Feng, et al. ∙ 13 ∙ share

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Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the O(1/T) Convergence Rate

Stochastic approximation (SA) is a classical approach for stochastic con...
01/27/2019 ∙ by Lijun Zhang, et al. ∙ 12 ∙ share

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Adaptive Regret of Convex and Smooth Functions

We investigate online convex optimization in changing environments, and ...
04/26/2019 ∙ by Lijun Zhang, et al. ∙ 10 ∙ share

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Expert-Level Atari Imitation Learning from Demonstrations Only

One of the key issues for imitation learning lies in making policy learn...
09/09/2019 ∙ by Xin-Qiang Cai, et al. ∙ 9 ∙ share

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Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness

Weakly supervised data are widespread and have attracted much attention....
04/22/2019 ∙ by Lan-Zhe Guo, et al. ∙ 6 ∙ share

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Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer the classifier learned f...
06/10/2019 ∙ by Dong-Dong Chen, et al. ∙ 5 ∙ share

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Bandit Convex Optimization in Non-stationary Environments

Bandit Convex Optimization (BCO) is a fundamental framework for modeling...
07/29/2019 ∙ by Peng Zhao, et al. ∙ 4 ∙ share

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Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions

To deal with changing environments, a new performance measure---adaptive...
06/26/2019 ∙ by Lijun Zhang, et al. ∙ 2 ∙ share

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Bifurcation Spiking Neural Network

Recently spiking neural networks (SNNs) have received much attention bec...
09/18/2019 ∙ by Shao-Qun Zhang, et al. ∙ 2 ∙ share

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Flexible Transmitter Network

Current neural networks are mostly built upon the MP model, which usuall...
04/08/2020 ∙ by Shao-Qun Zhang, et al. ∙ 1 ∙ share

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AutoEncoder by Forest

Auto-encoding is an important task which is typically realized by deep n...
09/26/2017 ∙ by Ji Feng, et al. ∙ 0 ∙ share

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A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions

Evolutionary algorithms (EAs) are population-based general-purpose optim...
06/10/2016 ∙ by Chao Qian, et al. ∙ 0 ∙ share

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Theoretical Foundation of Co-Training and Disagreement-Based Algorithms

Disagreement-based approaches generate multiple classifiers and exploit ...
08/15/2017 ∙ by Wei Wang, et al. ∙ 0 ∙ share

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Minimal Gated Unit for Recurrent Neural Networks

Recently recurrent neural networks (RNN) has been very successful in han...
03/31/2016 ∙ by Guo-Bing Zhou, et al. ∙ 0 ∙ share

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Multi-Label Learning with Global and Local Label Correlation

It is well-known that exploiting label correlations is important to mult...
04/04/2017 ∙ by Yue Zhu, et al. ∙ 0 ∙ share

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Learning with Feature Evolvable Streams

Learning with streaming data has attracted much attention during the pas...
06/16/2017 ∙ by Bo-Jian Hou, et al. ∙ 0 ∙ share

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Distribution-Free One-Pass Learning

In many large-scale machine learning applications, data are accumulated ...
06/08/2017 ∙ by Peng Zhao, et al. ∙ 0 ∙ share

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Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming

Reusable model design becomes desirable with the rapid expansion of comp...
07/20/2017 ∙ by Xiu-Shen Wei, et al. ∙ 0 ∙ share

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Crowdsourcing with Unsure Option

One of the fundamental problems in crowdsourcing is the trade-off betwee...
09/01/2016 ∙ by Yao-Xiang Ding, et al. ∙ 0 ∙ share

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Deep Forest: Towards An Alternative to Deep Neural Networks

In this paper, we propose gcForest, a decision tree ensemble approach wi...
02/28/2017 ∙ by Zhi-Hua Zhou, et al. ∙ 0 ∙ share

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Towards Analyzing Crossover Operators in Evolutionary Search via General Markov Chain Switching Theorem

Evolutionary algorithms (EAs), simulating the evolution process of natur...
11/03/2011 ∙ by Yang Yu, et al. ∙ 0 ∙ share

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On the approximation ability of evolutionary optimization with application to minimum set cover

Evolutionary algorithms (EAs) are heuristic algorithms inspired by natur...
11/17/2010 ∙ by Yang Yu, et al. ∙ 0 ∙ share

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Deep Descriptor Transforming for Image Co-Localization

Reusable model design becomes desirable with the rapid expansion of mach...
05/08/2017 ∙ by Xiu-Shen Wei, et al. ∙ 0 ∙ share

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Top Rank Optimization in Linear Time

Bipartite ranking aims to learn a real-valued ranking function that orde...
10/06/2014 ∙ by Nan Li, et al. ∙ 0 ∙ share

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Inductive Logic Boosting

Recent years have seen a surge of interest in Probabilistic Logic Progra...
02/25/2014 ∙ by Wang-Zhou Dai, et al. ∙ 0 ∙ share

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Analyzing Evolutionary Optimization in Noisy Environments

Many optimization tasks have to be handled in noisy environments, where ...
11/20/2013 ∙ by Chao Qian, et al. ∙ 0 ∙ share

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Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion

In this paper, we provide a theoretical analysis of the nuclear-norm reg...
04/26/2015 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

Deep convolutional neural network models pre-trained for the ImageNet cl...
04/18/2016 ∙ by Xiu-Shen Wei, et al. ∙ 0 ∙ share

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Dropout Rademacher Complexity of Deep Neural Networks

Great successes of deep neural networks have been witnessed in various r...
02/16/2014 ∙ by Wei Gao, et al. ∙ 0 ∙ share

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Efficient Optimization of Performance Measures by Classifier Adaptation

In practical applications, machine learning algorithms are often needed ...
12/04/2010 ∙ by Nan Li, et al. ∙ 0 ∙ share

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Exploiting Unlabeled Data to Enhance Ensemble Diversity

Ensemble learning aims to improve generalization ability by using multip...
09/19/2009 ∙ by Min-Ling Zhang, et al. ∙ 0 ∙ share

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Weak Evolvability Equals Strong Evolvability

An updated version will be uploaded later....
05/28/2009 ∙ by Yang Yu, et al. ∙ 0 ∙ share

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On the Consistency of AUC Pairwise Optimization

AUC (area under ROC curve) is an important evaluation criterion, which h...
08/03/2012 ∙ by Wei Gao, et al. ∙ 0 ∙ share

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Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters p...
04/05/2016 ∙ by Yuting Qiang, et al. ∙ 0 ∙ share

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Tunneling Neural Perception and Logic Reasoning through Abductive Learning

Perception and reasoning are basic human abilities that are seamlessly c...
02/04/2018 ∙ by Wang-Zhou Dai, et al. ∙ 0 ∙ share

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Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

Researchers often summarize their work in the form of scientific posters...
02/21/2017 ∙ by Yu-ting Qiang, et al. ∙ 0 ∙ share

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ℓ_1-regression with Heavy-tailed Distributions

In this paper, we consider the problem of linear regression with heavy-t...
05/02/2018 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Maximizing Non-monotone/Non-submodular Functions by Multi-objective Evolutionary Algorithms

Evolutionary algorithms (EAs) are a kind of nature-inspired general-purp...
11/20/2017 ∙ by Chao Qian, et al. ∙ 0 ∙ share

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Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud

Internet companies are facing the need of handling large scale machine l...
05/11/2018 ∙ by Ya-Lin Zhang, et al. ∙ 0 ∙ share

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Matrix Co-completion for Multi-label Classification with Missing Features and Labels

We consider a challenging multi-label classification problem where both ...
05/23/2018 ∙ by Miao Xu, et al. ∙ 0 ∙ share

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Multi-Layered Gradient Boosting Decision Trees

Multi-layered representation is believed to be the key ingredient of dee...
05/31/2018 ∙ by Ji Feng, et al. ∙ 0 ∙ share

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Handling Concept Drift via Model Reuse

In many real-world applications, data are often collected in the form of...
09/08/2018 ∙ by Peng Zhao, et al. ∙ 0 ∙ share

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Adaptive Online Learning in Dynamic Environments

In this paper, we study online convex optimization in dynamic environmen...
10/25/2018 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Optimal Margin Distribution Network

Recent research about margin theory has proved that maximizing the minim...
12/27/2018 ∙ by Shen-Huan Lv, et al. ∙ 0 ∙ share

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Learning with Interpretable Structure from RNN

In structure learning, the output is generally a structure that is used ...
10/25/2018 ∙ by Bo-Jian Hou, et al. ∙ 0 ∙ share

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Zhi-Hua Zhou

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