
Nonstationary Online Learning with Memory and Nonstochastic Control
We study the problem of Online Convex Optimization (OCO) with memory, wh...
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Isolation Distributional Kernel: A New Tool for Point Group Anomaly Detection
We introduce Isolation Distributional Kernel as a new way to measure the...
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Storage Fit Learning with Feature Evolvable Streams
Feature evolvable learning has been widely studied in recent years where...
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Dynamic Regret of Convex and Smooth Functions
We investigate online convex optimization in nonstationary environments...
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Soft Gradient Boosting Machine
Gradient Boosting Machine has proven to be one successful function appro...
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Flexible Transmitter Network
Current neural networks are mostly built upon the MP model, which usuall...
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Exploratory Machine Learning with Unknown Unknowns
In conventional supervised learning, a training dataset is given with gr...
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Model Reuse with Reduced Kernel Mean Embedding Specification
Given a publicly available pool of machine learning models constructed f...
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MultiLabel Learning with Deep Forest
In multilabel learning, each instance is associated with multiple label...
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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 (...
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Bifurcation Spiking Neural Network
Recently spiking neural networks (SNNs) have received much attention bec...
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ExpertLevel Atari Imitation Learning from Demonstrations Only
One of the key issues for imitation learning lies in making policy learn...
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Bandit Convex Optimization in Nonstationary Environments
Bandit Convex Optimization (BCO) is a fundamental framework for modeling...
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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 measureadaptive...
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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...
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Evaluating the Robustness of Nearest Neighbor Classifiers: A PrimalDual Perspective
We study the problem of computing the minimum adversarial perturbation o...
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Reinforcement Learning Experience Reuse with Policy Residual Representation
Experience reuse is key to sampleefficient reinforcement learning. One ...
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Learning to Confuse: Generating Training Time Adversarial Data with AutoEncoder
In this work, we consider one challenging training time attack by modify...
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Forest Representation Learning Guided by Margin Distribution
In this paper, we reformulate the forest representation learning approac...
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Prediction with Unpredictable Feature Evolution
Feature space can change or evolve when learning with streaming data. Se...
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Adaptive Regret of Convex and Smooth Functions
We investigate online convex optimization in changing environments, and ...
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Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
Weakly supervised data are widespread and have attracted much attention....
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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...
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Optimal Margin Distribution Network
Recent research about margin theory has proved that maximizing the minim...
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Adaptive Online Learning in Dynamic Environments
In this paper, we study online convex optimization in dynamic environmen...
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Learning with Interpretable Structure from RNN
In structure learning, the output is generally a structure that is used ...
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Handling Concept Drift via Model Reuse
In many realworld applications, data are often collected in the form of...
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MultiLayered Gradient Boosting Decision Trees
Multilayered representation is believed to be the key ingredient of dee...
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Matrix Cocompletion for Multilabel Classification with Missing Features and Labels
We consider a challenging multilabel classification problem where both ...
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Distributed Deep Forest and its Application to Automatic Detection of Cashout Fraud
Internet companies are facing the need of handling large scale machine l...
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ℓ_1regression with Heavytailed Distributions
In this paper, we consider the problem of linear regression with heavyt...
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Tunneling Neural Perception and Logic Reasoning through Abductive Learning
Perception and reasoning are basic human abilities that are seamlessly c...
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Maximizing Nonmonotone/Nonsubmodular Functions by Multiobjective Evolutionary Algorithms
Evolutionary algorithms (EAs) are a kind of natureinspired generalpurp...
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AutoEncoder by Forest
Autoencoding is an important task which is typically realized by deep n...
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Theoretical Foundation of CoTraining and DisagreementBased Algorithms
Disagreementbased approaches generate multiple classifiers and exploit ...
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Unsupervised Object Discovery and CoLocalization by Deep Descriptor Transforming
Reusable model design becomes desirable with the rapid expansion of comp...
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Learning with Feature Evolvable Streams
Learning with streaming data has attracted much attention during the pas...
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DistributionFree OnePass Learning
In many largescale machine learning applications, data are accumulated ...
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Deep Descriptor Transforming for Image CoLocalization
Reusable model design becomes desirable with the rapid expansion of mach...
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MultiLabel Learning with Global and Local Label Correlation
It is wellknown that exploiting label correlations is important to mult...
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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...
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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...
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Crowdsourcing with Unsure Option
One of the fundamental problems in crowdsourcing is the tradeoff betwee...
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A Lower Bound Analysis of Populationbased Evolutionary Algorithms for PseudoBoolean Functions
Evolutionary algorithms (EAs) are populationbased generalpurpose optim...
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Selective Convolutional Descriptor Aggregation for FineGrained Image Retrieval
Deep convolutional neural network models pretrained for the ImageNet cl...
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Learning to Generate Posters of Scientific Papers
Researchers often summarize their work in the form of posters. Posters p...
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Minimal Gated Unit for Recurrent Neural Networks
Recently recurrent neural networks (RNN) has been very successful in han...
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Analysis of Nuclear Norm Regularization for Fullrank Matrix Completion
In this paper, we provide a theoretical analysis of the nuclearnorm reg...
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Top Rank Optimization in Linear Time
Bipartite ranking aims to learn a realvalued ranking function that orde...
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Inductive Logic Boosting
Recent years have seen a surge of interest in Probabilistic Logic Progra...
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