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Found 17 papers, 4 papers with code
Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE
To address the problem, we propose a constraint utilizing symmetric InfoNCE, which helps an objective of deep clustering method in the scenario train the model so as to be efficient for not only non-complex topology but also complex topology datasets.
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
Domain shifts in the training data are common in practical applications of machine learning, they occur for instance when the data is coming from different sources.
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation
We report the computational efficiency and statistical reliability of our method in numerical experiments of the language modeling using RNNs, and the out-of-distribution detection with DNNs.
Robust modal regression with direct log-density derivative estimation
In order to apply a gradient method for the maximization, the fundamental challenge is accurate approximation of the gradient of MRR, not MRR itself.
Estimating Density Models with Truncation Boundaries using Score Matching
In this paper, we study parameter estimation for truncated probability densities using SM.
Unified estimation framework for unnormalized models with statistical efficiency
The parameter estimation of unnormalized models is a challenging problem.
Variable Selection for Nonparametric Learning with Power Series Kernels
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation.
Fisher Efficient Inference of Intractable Models
For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{\'e}r-Rao lower bound (efficiency bound), which is the minimum possible variance for an unbiased estimator.
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
Based on the proposed estimator, novel methods both for mode-seeking clustering and density ridge estimation are developed, and the respective convergence rates to the mode and ridge of the underlying density are also established.
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
In this paper, we propose a novel parameter estimator for probabilistic models on discrete space.
Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle
Our algorithm consists of two steps; one is the direction estimate step and the other is the search step.
Breakdown Point of Robust Support Vector Machine
For learning parameters such as the regularization parameter in our algorithm, we derive a simple formula that guarantees the robustness of the classifier.
Affine Invariant Divergences associated with Composite Scores and its Applications
By using the equivariant estimators under the affine transformation, one can obtain estimators that do no essentially depend on the choice of the system of units in the measurement.
Density-Difference Estimation
A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference.
Relative Density-Ratio Estimation for Robust Distribution Comparison
Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution comparison such as outlier detection, transfer learning, and two-sample homogeneity test.
Condition Number Analysis of Kernel-based Density Ratio Estimation
We show that the kernel least-squares method has a smaller condition number than a version of kernel mean matching and other M-estimators, implying that the kernel least-squares method has preferable numerical properties.
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection
We address the problem of estimating the ratio of two probability density functions (a. k. a.~the importance).
