
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
Pairwise similarities and dissimilarities between data points might be e...
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Online Multiclass Classification Based on Prediction Margin for Partial Feedback
We consider the problem of online multiclass classification with partial...
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Imitation Learning from Imperfect Demonstration
Imitation learning (IL) aims to learn an optimal policy from demonstrati...
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Solving NPHard Problems on Graphs by Reinforcement Learning without Domain Knowledge
We propose an algorithm based on reinforcement learning for solving NPh...
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Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PACBayesian Analysis
The notion of flat minima has played a key role in the generalization pr...
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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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Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
It is challenging to train deep neural networks robustly on the industri...
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SemiSupervised Ordinal Regression Based on Empirical Risk Minimization
We consider the semisupervised ordinal regression problem, where unlabe...
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Active Deep Qlearning with Demonstration
Recent research has shown that although Reinforcement Learning (RL) can ...
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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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Unsupervised Domain Adaptation Based on Sourceguided Discrepancy
Unsupervised domain adaptation is the problem setting where data generat...
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Classification from Triplet Comparison Data
Learning from triplet comparison data has been extensively studied in th...
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Hierarchical Policy Search via ReturnWeighted Density Estimation
Learning an optimal policy from a multimodal reward function is a chall...
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Binary Classification from PositiveConfidence Data
Reducing labeling costs in supervised learning is a critical issue in ma...
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Variational Inference based on Robust Divergences
Robustness to outliers is a central issue in realworld machine learning...
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Good Arm Identification via Bandit Feedback
In this paper, we consider and discuss a new stochastic multiarmed band...
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Fully adaptive algorithm for pure exploration in linear bandits
We propose the first fullyadaptive algorithm for pure exploration in li...
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Estimation of SquaredLoss Mutual Information from Positive and Unlabeled Data
Capturing inputoutput dependency is an important task in statistical da...
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ModeSeeking Clustering and Density Ridge Estimation via Direct Estimation of DensityDerivativeRatios
Modes and ridges of the probability density function behind observed dat...
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Expectation Propagation for tExponential Family Using QAlgebra
Exponential family distributions are highly useful in machine learning s...
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Deep Reinforcement Learning with Relative Entropy Stochastic Search
Many reinforcement learning methods for continuous control tasks are bas...
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Learning from Complementary Labels
Collecting labeled data is costly and thus a critical bottleneck in real...
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Bayesian Nonparametric PoissonProcess Allocation for TimeSequence Modeling
Analyzing the underlying structure of multiple timesequences provides i...
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SemiSupervised AUC Optimization based on PositiveUnlabeled Learning
Maximizing the area under the receiver operating characteristic curve (A...
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Stochastic Divergence Minimization for Biterm Topic Model
As the emergence and the thriving development of social networks, a huge...
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PositiveUnlabeled Learning with NonNegative Risk Estimator
From only positive (P) and unlabeled (U) data, a binary classifier could...
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Learning Discrete Representations via Information Maximizing SelfAugmented Training
Learning discrete representations of data is a central machine learning ...
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Policy Search with HighDimensional Context Variables
Direct contextual policy search methods learn to improve policy paramete...
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Revisiting Distributionally Robust Supervised Learning in Classification
Distributionally Robust Supervised Learning (DRSL) is necessary for buil...
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Classprior Estimation for Learning from Positive and Unlabeled Data
We consider the problem of estimating the class prior in an unlabeled da...
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Theoretical Comparisons of PositiveUnlabeled Learning against PositiveNegative Learning
In PU learning, a binary classifier is trained from positive (P) and unl...
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WhiteningFree LeastSquares NonGaussian Component Analysis
NonGaussian component analysis (NGCA) is an unsupervised linear dimensi...
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NonGaussian Component Analysis with LogDensity Gradient Estimation
NonGaussian component analysis (NGCA) is aimed at identifying a linear ...
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Faster Stochastic Variational Inference using ProximalGradient Methods with General Divergence Functions
Several recent works have explored stochastic gradient methods for varia...
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Theoretical and Experimental Analyses of TensorBased Regression and Classification
We theoretically and experimentally investigate tensorbased regression ...
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Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction
A typical goal of supervised dimension reduction is to find a lowdimens...
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Regularized MultiTask Learning for MultiDimensional LogDensity Gradient Estimation
Logdensity gradient estimation is a fundamental statistical problem and...
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Structure Learning of Partitioned Markov Networks
We learn the structure of a Markov Network between two groups of random ...
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Reinterpreting the Transformation Posterior in Probabilistic Image Registration
Probabilistic image registration methods estimate the posterior distribu...
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Support Consistency of Direct SparseChange Learning in Markov Networks
We study the problem of learning sparse structure changes between two Ma...
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Direct DensityDerivative Estimation and Its Application in KLDivergence Approximation
Estimation of density derivatives is a versatile tool in statistical dat...
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Conditional Density Estimation with Dimensionality Reduction via SquaredLoss Conditional Entropy Minimization
Regression aims at estimating the conditional mean of output given input...
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Clustering via Mode Seeking by Direct Estimation of the Gradient of a LogDensity
Mean shift clustering finds the modes of the data probability density by...
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Transductive Learning with Multiclass Volume Approximation
Given a hypothesis space, the large volume principle by Vladimir Vapnik ...
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Support vector comparison machines
In ranking problems, the goal is to learn a ranking function from labele...
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ModelBased Policy Gradients with ParameterBased Exploration by LeastSquares Conditional Density Estimation
The goal of reinforcement learning (RL) is to let an agent learn an opti...
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SemiSupervised InformationMaximization Clustering
Semisupervised clustering aims to introduce prior knowledge in the deci...
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Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
We propose a new method for detecting changes in Markov network structur...
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Density Ratio Hidden Markov Models
Hidden Markov models and their variants are the predominant sequential c...
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Efficient Sample Reuse in Policy Gradients with Parameterbased Exploration
The policy gradient approach is a flexible and powerful reinforcement le...
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Masashi Sugiyama
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Director  RIKEN Center for Advanced Intelligence Project, Professor at University of Tokyo