
A Symmetric Loss Perspective of Reliable Machine Learning
When minimizing the empirical risk in binary classification, it is a com...
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Combinatorial Pure Exploration with Fullbandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation
Combinatorial optimization is one of the fundamental research fields tha...
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Stable Weight Decay Regularization
Weight decay is a popular regularization technique for training of deep ...
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On Focal Loss for ClassPosterior Probability Estimation: A Theoretical Perspective
The focal loss has demonstrated its effectiveness in many realworld app...
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Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting
Deep learning is often criticized by two serious issues which rarely exi...
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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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Binary classification with ambiguous training data
In supervised learning, we often face with ambiguous (A) samples that ar...
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Classification with Rejection Based on Costsensitive Classification
The goal of classification with rejection is to avoid risky misclassific...
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Maximum Mean Discrepancy is Aware of Adversarial Attacks
The maximum mean discrepancy (MMD) test, as a representative twosample ...
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Robust Imitation Learning from Noisy Demonstrations
Learning from noisy demonstrations is a practical but highly challenging...
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Pointwise Binary Classification with Pairwise Confidence Comparisons
Ordinary (pointwise) binary classification aims to learn a binary classi...
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Geometryaware Instancereweighted Adversarial Training
In adversarial machine learning, there was a common belief that robustne...
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Provably Consistent PartialLabel Learning
Partiallabel learning (PLL) is a multiclass classification problem, wh...
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A Onestep Approach to Covariate Shift Adaptation
A default assumption in many machine learning scenarios is that the trai...
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Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
In weakly supervised learning, unbiased risk estimator(URE) is a powerfu...
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Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia
Adaptive Momentum Estimation (Adam), which combines Adaptive Learning Ra...
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Online Dense Subgraph Discovery via BlurredGraph Feedback
Dense subgraph discovery aims to find a dense component in edgeweighted...
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Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent
In continual learning settings, deep neural networks are prone to catast...
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Couplingbased Invertible Neural Networks Are Universal Diffeomorphism Approximators
Invertible neural networks based on coupling flows (CFINNs) have variou...
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Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
We investigate finite stochastic partial monitoring, which is a general ...
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LFDProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Fewshot Learning
The prototypical network (ProtoNet) is a fewshot learning framework tha...
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Partsdependent Label Noise: Towards Instancedependent Label Noise
Learning with the instancedependent label noise is challenging, because...
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Dual T: Reducing Estimation Error for Transition Matrix in Labelnoise Learning
The transition matrix, denoting the transition relationship from clean l...
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Similaritybased Classification: Connecting Similarity Learning to Binary Classification
In realworld classification problems, pairwise supervision (i.e., a pai...
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Rethinking Importance Weighting for Deep Learning under Distribution Shift
Under distribution shift (DS) where the training data distribution diffe...
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Calibrated Surrogate Losses for Adversarially Robust Classification
Adversarially robust classification seeks a classifier that is insensiti...
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Learning from Aggregate Observations
We study the problem of learning from aggregate observations where super...
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Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation?
With the increasing availability of new image registration approaches, a...
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Timevarying Gaussian Process Bandit Optimization with Nonconstant Evaluation Time
The Gaussian process bandit is a problem in which we want to find a maxi...
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Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Adversarial training based on the minimax formulation is necessary for o...
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Do We Need Zero Training Loss After Achieving Zero Training Error?
Overparameterized deep networks have the capacity to memorize training d...
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Progressive Identification of True Labels for PartialLabel Learning
Partiallabel learning is one of the important weakly supervised learnin...
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Towards Mixture Proportion Estimation without Irreducibility
Mixture proportion estimation (MPE) is a fundamental problem of practica...
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Fewshot Domain Adaptation by Causal Mechanism Transfer
We study fewshot supervised domain adaptation (DA) for regression probl...
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A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast
Stochastic optimization algorithms, such as Stochastic Gradient Descent ...
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Learning from Noisy Similar and Dissimilar Data
With the widespread use of machine learning for classification, it becom...
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Binary Classification from Positive Data with Skewed Confidence
Positiveconfidence (Pconf) classification [Ishida et al., 2018] is a pr...
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Confidence Scores Make Instancedependent Labelnoise Learning Possible
Learning with noisy labels has drawn a lot of attention. In this area, m...
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Where is the Bottleneck of Adversarial Learning with Unlabeled Data?
Deep neural networks (DNNs) are incredibly brittle due to adversarial ex...
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Scalable Evaluation and Improvement of Document Set Expansion via Neural PositiveUnlabeled Learning
We consider the situation in which a user has collected a small set of d...
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Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
From two unlabeled (U) datasets with different class priors, we can trai...
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A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme
Reparameterization (RP) and likelihood ratio (LR) gradient estimators ar...
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Learning from Indirect Observations
Weaklysupervised learning is a paradigm for alleviating the scarcity of...
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Learning Only from Relevant Keywords and Unlabeled Documents
We consider a document classification problem where document labels are ...
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Reducing Overestimation Bias in MultiAgent Domains Using Double Centralized Critics
Many real world tasks require multiple agents to work together. Multiag...
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VILD: Variational Imitation Learning with Diversequality Demonstrations
The goal of imitation learning (IL) is to learn a good policy from high...
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Pilot Study on Verifying the Monotonic Relationship between Error and Uncertainty in Deformable Registration for Neurosurgery
In imageguided neurosurgery, deformable registration currently is not a...
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Classification from Triplet Comparison Data
Learning from triplet comparison data has been extensively studied in th...
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Direction Matters: On InfluencePreserving Graph Summarization and Maxcut Principle for Directed Graphs
Summarizing largescaled directed graphs into smallscale representation...
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Are Anchor Points Really Indispensable in LabelNoise Learning?
In labelnoise learning, noise transition matrix, denoting the probabili...
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Masashi Sugiyama
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Director  RIKEN Center for Advanced Intelligence Project, Professor at University of Tokyo