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Found 20 papers, 5 papers with code
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).
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.
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.
Density-Difference Estimation
A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference.
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.
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.
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.
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
In this paper, we propose a novel parameter estimator for probabilistic models on discrete space.
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.
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.
Variable Selection for Nonparametric Learning with Power Series Kernels
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation.
Unified estimation framework for unnormalized models with statistical efficiency
The parameter estimation of unnormalized models is a challenging problem.
Estimating Density Models with Truncation Boundaries using Score Matching
In this paper, we study parameter estimation for truncated probability densities using SM.
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.
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.
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
However, common ML losses do not give strong guarantees on how consistently the ML model performs for different domains, in particular, whether the model performs well on a domain at the expense of its performance on another domain.
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.
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis
We first prove that the formulation characterizes the structure of representations learned with the kernel-based contrastive learning framework.
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
To this end, inspired by recent works on denoising and the success of the cosine-similarity-based objective functions in representation learning, we propose the denoising Cosine-Similarity (dCS) loss.
A Convex Framework for Confounding Robust Inference
We study policy evaluation of offline contextual bandits subject to unobserved confounders.
