
The Emerging Trends of MultiLabel Learning
Exabytes of data are generated daily by humans, leading to the growing n...
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A Survey of Labelnoise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the trainin...
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Learning on AttributeMissing Graphs
Graphs with complete node attributes have been widely explored recently....
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Collaborative Generative Hashing for Marketing and Fast Coldstart Recommendation
Coldstart has being a critical issue in recommender systems with the ex...
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Deep Pairwise Hashing for Coldstart Recommendation
Recommendation efficiency and data sparsity problems have been regarded ...
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Graph Cross Networks with Vertex Infomax Pooling
We propose a novel graph cross network (GXN) to achieve comprehensive fe...
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Intrinsic Reward Driven Imitation Learning via Generative Model
Imitation learning in a highdimensional environment is challenging. Mos...
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Multiview Alignment and Generation in CCA via Consistent Latent Encoding
Multiview alignment, achieving onetoone correspondence of multiview ...
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Secure Metric Learning via Differential Pairwise Privacy
Distance Metric Learning (DML) has drawn much attention over the last tw...
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Face Hallucination with Finishing Touches
Obtaining a highquality frontal face image from a lowresolution (LR) n...
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Towards Sharper FirstOrder Adversary with Quantized Gradients
Despite the huge success of Deep Neural Networks (DNNs) in a wide spectr...
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Stochastic Implicit Natural Gradient for Blackbox Optimization
Blackbox optimization is primarily important for many computeintensive...
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Improving Generalization via Attribute Selection on Outofthebox Data
Zeroshot learning (ZSL) aims to recognize unseen objects (test classes)...
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Node Attribute Generation on Graphs
Graph structured data provide twofold information: graph structures and...
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Latent Adversarial Defence with Boundaryguided Generation
Deep Neural Networks (DNNs) have recently achieved great success in many...
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Fast and Robust Rank Aggregation against Model Misspecification
In rank aggregation, preferences from different users are summarized int...
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Curriculum Loss: Robust Learning and Generalization against Label Corruption
Generalization is vital important for many deep network models. It becom...
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Efficient Batch Blackbox Optimization with Deterministic Regret Bounds
In this work, we investigate blackbox optimization from the perspective...
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Marginalized Average Attentional Network for WeaklySupervised Learning
In weaklysupervised temporal action localization, previous works have f...
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Learning ImageSpecific Attributes by Hyperbolic Neighborhood Graph Propagation
As a kind of semantic representation of visual object descriptions, attr...
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Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Learning with noisy labels, which aims to reduce expensive labors on acc...
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How Does Disagreement Benefit Coteaching?
Learning with noisy labels is one of the most important question in weak...
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A Survey on Multioutput Learning
Multioutput learning aims to simultaneously predict multiple outputs gi...
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Privacypreserving Stochastic Gradual Learning
It is challenging for stochastic optimizations to handle largescale sen...
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TargetIndependent Active Learning via DistributionSplitting
To reduce the label complexity in Agnostic Active Learning (A^2 algorith...
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A Structured Perspective of Volumes on Active Learning
Active Learning (AL) is a learning task that requires learners interacti...
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Understanding VAEs in FisherShannon Plane
In information theory, Fisher information and Shannon information (entro...
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Geometric Active Learning via Enclosing Ball Boundary
Active Learning (AL) requires learners to retrain the classifier with th...
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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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MultiContext Label Embedding
Label embedding plays an important role in zeroshot learning. Side info...
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Zeroshot Learning with Complementary Attributes
Zeroshot learning (ZSL) aims to recognize unseen objects using disjoint...
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VRSGD: A Simple Stochastic Variance Reduction Method for Machine Learning
In this paper, we propose a simple variant of the original SVRG, called ...
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Millionaire: A Hintguided Approach for Crowdsourcing
Modern machine learning is migrating to the era of complex models, which...
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Degeneration in VAE: in the Light of Fisher Information Loss
Variational Autoencoder (VAE) is one of the most popular generative mode...
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Online Product Quantization
Approximate nearest neighbor (ANN) search has achieved great success in ...
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Transfer Hashing with Privileged Information
Most existing learning to hash methods assume that there are sufficient ...
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A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation
Many scientific datasets are of high dimension, and the analysis usually...
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Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals
Matching Pursuit LASSIn Part I TanPMLPart1, a Matching Pursuit LASSO (MP...
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ParameterFree Spectral Kernel Learning
Due to the growing ubiquity of unlabeled data, learning with unlabeled d...
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Hierarchical Maximum Margin Learning for MultiClass Classification
Due to myriads of classes, designing accurate and efficient classifiers ...
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Efficient Optimization of Performance Measures by Classifier Adaptation
In practical applications, machine learning algorithms are often needed ...
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Ivor W. Tsang
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