Papers Density Ratio Estimation
“Density Ratio Estimation” 태그가 달린 논문 93편 · 필터 해제
Savage-Dickey density ratio estimation with normalizing flows for Bayesian model comparison
A core motivation of science is to evaluate which scientific model best explains observed data. Bayesian model comparison provides a principled statistical approach to comparing scientific models and has found widespread…
Density Ratio EstimationProjection Pursuit Density Ratio Estimation
Density ratio estimation (DRE) is a paramount task in machine learning, for its broad applications across multiple domains, such as covariate shift adaptation, causal inference, independence tests and beyond. Parametric …
Causal InferenceDensity Ratio EstimationDensity Ratio-Free Doubly Robust Proxy Causal Learning
We study the problem of causal function estimation in the Proxy Causal Learning (PCL) framework, where confounders are not observed but proxies for the confounders are available. Two main approaches have been proposed: o…
Density Ratio EstimationDequantified Diffusion-Schr{ö}dinger Bridge for Density Ratio Estimation
Density ratio estimation is fundamental to tasks involving $f$-divergences, yet existing methods often fail under significantly different distributions or inadequately overlap supports, suffering from the density-chasm a…
Density EstimationDensity Ratio EstimationDensity Ratio Estimation with Conditional Probability Paths
Density ratio estimation in high dimensions can be reframed as integrating a certain quantity, the time score, over probability paths which interpolate between the two densities. In practice, the time score has to be est…
Density Ratio EstimationLearning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test
Time-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT), which provides an optimal stopping time for early classification of time series. However, in finite horizon scenarios, where input l…
Density Ratio EstimationEarly ClassificationYour copula is a classifier in disguise: classification-based copula density estimation
We propose reinterpreting copula density estimation as a discriminative task. Under this novel estimation scheme, we train a classifier to distinguish samples from the joint density from those of the product of independe…
Density EstimationDensity Ratio EstimationHow to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective
This paper introduces a novel generalized self-imitation learning ($\textbf{GSIL}$) framework, which effectively and efficiently aligns large language models with offline demonstration data. We develop $\textbf{GSIL}$ by…
Density Ratio EstimationGSM8KImitation LearningInstruction Following+4Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting
Climate change-driven floods demand advanced forecasting models, yet Graph Neural Networks (GNNs) underutilize river network topology due to tree-like structures causing over-squashing from high node resistance distances…
Cloth-Changing Person Re-IdentificationDensity Ratio EstimationFeature CorrelationPerson Re-IdentificationBounds on $L_p$ Errors in Density Ratio Estimation via $f$-Divergence Loss Functions
Density ratio estimation (DRE) is a fundamental machine learning technique for identifying relationships between two probability distributions. $f$-divergence loss functions, derived from variational representations of $…
Density Ratio EstimationA density ratio framework for evaluating the utility of synthetic data
Synthetic data generation is a promising technique to facilitate the use of sensitive data while mitigating the risk of privacy breaches. However, for synthetic data to be useful in downstream analysis tasks, it needs to…
Density Ratio EstimationSynthetic Data GenerationRobust Conformal Volume Estimation in 3D Medical Images
Volumetry is one of the principal downstream applications of 3D medical image segmentation, for example, to detect abnormal tissue growth or for surgery planning. Conformal Prediction is a promising framework for uncerta…
Conformal PredictionDensity Ratio EstimationImage SegmentationMedical Image Segmentation+2Binary Losses for Density Ratio Estimation
Estimating the ratio of two probability densities from a finite number of observations is a central machine learning problem. A common approach is to construct estimators using binary classifiers that distinguish observa…
Density Ratio EstimationDomain AdaptationDensity Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds
The density ratio of two probability distributions is one of the fundamental tools in mathematical and computational statistics and machine learning, and it has a variety of known applications. Therefore, density ratio e…
Density Ratio EstimationMeta-learning for Positive-unlabeled Classification
We propose a meta-learning method for positive and unlabeled (PU) classification, which improves the performance of binary classifiers obtained from only PU data in unseen target tasks. PU learning is an important proble…
ClassificationDensity Ratio EstimationInformation RetrievalMeta-Learning+1Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space
Offline Black-Box Optimization (BBO) aims at optimizing a black-box function using the knowledge from a pre-collected offline dataset of function values and corresponding input designs. However, the high-dimensional and …
Density Ratio EstimationOvercoming Saturation in Density Ratio Estimation by Iterated Regularization
Estimating the ratio of two probability densities from finitely many samples, is a central task in machine learning and statistics. In this work, we show that a large class of kernel methods for density ratio estimation …
Density Ratio EstimationDomain AdaptationUnsupervised Domain AdaptationClassification Diffusion Models: Revitalizing Density Ratio Estimation
A prominent family of methods for learning data distributions relies on density ratio estimation (DRE), where a model is trained to $\textit{classify}$ between data samples and samples from some reference distribution. D…
Audio GenerationClassificationDenoisingDensity Ratio Estimation+1Alpha-divergence loss function for neural density ratio estimation
Density ratio estimation (DRE) is a fundamental machine learning technique for capturing relationships between two probability distributions. State-of-the-art DRE methods estimate the density ratio using neural networks …
Density Ratio EstimationDouble Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
Consider a scenario where we have access to train data with both covariates and outcomes while test data only contains covariates. In this scenario, our primary aim is to predict the missing outcomes of the test data. Wi…
Density Ratio Estimationregression