Direct neural ratio estimator for likelihood-free inference.
problem Efficient likelihood estimation for complex models.
method Amortized likelihood ratio estimation using neural networks.
result DNRE often outperforms previous ratio estimators.
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48 results for “likelihood ratio method”
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimationstat.ML
Paper detects changes in graph-based data streams using likelihood-ratios.
problem Detecting changes in synchronized graph-based data streams.
method Kernel-based likelihood-ratio estimation over graph nodes.
result Effective detection and localization of change-points.
Novel MCMC method tackles intractable likelihoods using learned ratio estimators.
problem Posterior inference with intractable likelihoods in complex simulations.
method Amortized approximate ratio estimator embedded in MCMC samplers.
result Effective approximation of likelihood-ratios for sampling from intractable posterior.
Novel neural likelihood ratio estimation for negative data in particle physics.
problem Estimating likelihood ratios with negative probability densities and weights.
method Introducing a novel loss function and a new model architecture based on signed mixture models.
result Demonstrated improved estimation on a real-world example from particle physics.
New machine learning methods for inference from simulated data.
problem Modeling score and likelihood ratio functions from sampled data.
method InferoStatic Networks (ISN), Kernel Score Estimation (KSE), Kernel Likelihood Ratio Estimation (KLRE).
result Improved inference methods for complex models.
Neural networks approximate likelihood ratios for complex models.
problem Difficulty in computing likelihood ratios for modern models.
method Applying the likelihood ratio trick with neural network classifiers.
result Different neural network setups can approximate likelihood ratios with varying performance.
Develops neural network methods for likelihood ratio estimation.
problem Estimating likelihood ratios from data.
method Neural network training for likelihood ratio estimation.
result Unified methodology for defining and solving optimization problems.
The paper proposes a method to construct confidence sets using likelihood ratios for sequential decision-making.
problem Constructing valid uncertainty estimates for unknown quantities in sequential decision-making.
method The method uses likelihood ratios to create any-time valid confidence sequences without specialized treatment for each application.
result The proposed confidence sets maintain the prescribed coverage in a model-agnostic manner and their size depends on the choice of estimator sequence.
Deep learning compresses and quantizes log-likelihood ratios for fading channels.
problem Efficiently compress and quantize log-likelihood ratios for fading channels.
method Trains a deep autoencoder network to map log-likelihood ratios to a latent space and reconstruct them.
result Achieves a compression factor of nearly three times with minimal performance loss.
Optimal selective classification using likelihood ratios improves model reliability.
problem Enhancing predictive model reliability by allowing uncertain predictions.
method Neyman--Pearson lemma applied to likelihood ratios for optimal selection.
result Neyman--Pearson-informed methods outperform existing baselines under covariate shifts.
Universal Inferencemath.ST
A universal method for hypothesis tests and confidence sets without regularity conditions.
problem Difficult inference in irregular statistical models.
method Modified likelihood ratio statistic (split LRT).
result Works for any parametric and some nonparametric models.
Extends likelihood ratio exponential families to analyze various optimization methods.
problem Analyzing optimization methods like rate-distortion and information bottleneck.
method Linking geometric mixture paths to exponential families and using hypothesis testing.
result Provides a common mathematical framework for understanding these methods.
Study shows how to reduce variational inference bias by concentrating likelihood ratio distribution.
problem Bias and variance issues in variational inference.
method Upper bound variational gap using dispersion measure of likelihood ratio, suggesting methods to reduce bias.
result Reducing bias in variational inference can be achieved by making likelihood ratio distribution more concentrated.
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignmentcs.LG
New method for robust distribution alignment using log-likelihood ratio and normalizing flows.
problem Distribution alignment challenges in deep learning.
method Log-likelihood ratio statistic and normalizing flows.
result Minimizing the proposed objective yields robust domain alignment.
DeepLR constructs confidence intervals for neural networks with asymmetric expansions.
problem Uncertainty estimation for neural network predictions.
method Likelihood-ratio-based approach for constructing asymmetric confidence intervals.
result DeepLR offers asymmetric intervals expanding in regions with limited data.
In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generative model) is used to describe complex processes that tie parameters of an underlying theory and measurement apparatus to hig…
A new likelihood ratio metric for GANs training stability.
problem Training consistency and stability in GANs.
method Likelihood ratio approach for adversarial optimization.
result New metric for online convergence and stability assessment.
DIRE uses neural networks to estimate summary statistics for likelihood-free inference.
problem Difficulty in learning parameters from observed data due to intractable likelihood functions.
method DIRE uses convolutional neural networks to estimate summary statistics for likelihood-free inference.
result A single neural network architecture can produce equally or more accurate posteriors than alternative methods.
Unified view of KL-divergence and IPMs via DRE, with new DRM metrics.
problem Unified understanding of KL-divergence and IPMs.
method Unified representation via maximum likelihood density-ratio estimation (DRE).
result Unified form of IPMs and novel DRM metrics.
Bayesian optimization improves by focusing on outputs with the likelihood ratio method.
problem Improving Bayesian optimization by accurately estimating output importance.
method Importance-sampling theory and likelihood ratio for guiding search towards low objective function values.
result Likelihood-weighted acquisition functions outperform unweighted ones in various applications.
New method improves neural network robustness with brain-like learning.
problem Traditional methods struggle with robustness in adversarial attacks.
method Generalized likelihood ratio method with brain-like learning mechanisms.
result Significant improvement in robustness of neural networks.
The paper proposes an efficient nested simulation design using likelihood ratio method.
problem Designing nested simulations with fixed outer scenarios and minimizing simulation effort.
method Proposes a bi-level optimization problem to decide inner replications and pooling strategies.
result Optimized design achieves $\cO(Γ^{-1})$ mean squared error of estimators.
Graph-based LRE estimates likelihood-ratios collaboratively for nodes.
problem Comparing unknown pdfs at graph nodes with graph structure.
method Graph-based Relative Unconstrained Least-squares Importance Fitting (GRULSIF).
result Collaborative estimation improves performance compared to independent methods.
The paper exposes common misconceptions about OOD detection and proposes a new framework.
problem Density-based OOD detection fails in deep learning settings.
method Proposes the OOD proxy framework to unify likelihood-ratio-based methods.
result Likelihood ratio is a principled method for OOD detection.
A new method optimizes diffusion models with recursive likelihood ratios.
problem Efficiently aligning pre-trained diffusion models for specific applications.
method Recursive Likelihood Ratio (RLR) optimizer for Half-Order (HO) fine-tuning.
result The RLR method achieves unbiased and lower-variance gradients, improving model performance.
A new method combines classification with population Monte Carlo for efficient ABC.
problem Inefficient particle proposals and subjectivity in ABC methods.
method Classification-PMC, blending adaptive proposals and classification.
result Classification-PMC outperforms state-of-the-art ABC methods in simulations.
Conformal Inference under High-Dimensional Covariate Shifts via Likelihood-Ratio Regularizationstat.ML
New method for valid prediction sets in high-dimensional covariate shifts.
problem Valid prediction sets in high-dimensional covariate shifts.
method Likelihood-ratio regularized quantile regression (LR-QR) algorithm.
result LR-QR constructs valid prediction sets with desired coverage in target domain.
Study detects signals in spiked Wigner models using log likelihood ratio.
problem Detecting signals in rank-one spiked Wigner models with non-Gaussian noise.
method Proved asymptotic normality of log likelihood ratio and computed error thresholds.
result Optimal signal-to-noise ratio threshold for reliable detection.
The ratio of two probability densities can be used for solving various machine learning tasks such as covariate shift adaptation (importance sampling), outlier detection (likelihood-ratio test), and feature selection (mutual information). Recently, several methods of directly estimating the density ratio have been deve…
A novel kernel-based test detects equality versus singularity of two probability measures.
problem Detecting equality versus singularity of two probability distributions.
method Combines kernel mean and kernel covariance embeddings to construct a likelihood ratio test statistic.
result The test statistic satisfies a '0/\infty' law, vanishing under the null and diverging under the alternative.
A new method improves text generation quality and diversity.
problem Exposure bias in Maximum Likelihood Estimation for text generation.
method ψ-MLE, a new training scheme based on density ratio estimation.
result ψ-MLE outperforms Maximum Likelihood Estimation and other models in text generation quality and diversity.
New method uses path signatures for efficient likelihood estimation in time-series data.
problem Intractable likelihood functions in complex dynamic models.
method Kernel classifier based on path signatures for sequential data.
result Path signatures yield highly performant classifiers, even with low sample numbers.
New method estimates hazard ratios without bias in observational studies.
problem Uninterpretable hazard ratios due to unspecified baseline hazard.
method Kernel-based machine learning to model risk set changes.
result Debiased maximum-likelihood estimators identify true hazard ratios.
Optimally tackles covariate shift in RKHS-based nonparametric regression.
problem Covariate shift in nonparametric regression over RKHS.
method Two families of covariate shift problems defined using likelihood ratios. Minimax rate-optimal estimators for KRR and reweighted KRR.
result KRR is minimax rate-optimal and strictly sub-optimal compared to naive estimator under covariate shift.
wd1 improves reasoning in dLLMs by optimizing policies without policy ratios.
problem Improving reasoning in diffusion-based large language models through RL.
method wd1: ratio-free policy optimization using weighted log-likelihood.
result wd1 outperforms diffusion-based GRPO while requiring lower computational cost.
Unified view of LR and RP gradients explained via divergence theorem.
problem Explaining the nature and relationship of LR and RP gradients.
method First principles approach using divergence theorem.
result Characterization of all possible estimators combining LR and RP.
Method corrects deep generative model likelihood scores for OOD detection.
problem Challenges in deploying neural networks on out-of-distribution data.
method Likelihood ratio method for deep generative models.
result Proposed method achieves state-of-the-art OOD detection performance.
A method detects changes in heterogeneous data streams over graph nodes.
problem Detecting changes in data streams from nodes of a graph.
method Online non-parametric method using likelihood-ratio estimation.
result The method accurately identifies change-points in real-world applications.
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimizationstat.ML
Paper introduces an online method for estimating the difference between two probability distributions.
problem Estimating the difference between two probability density functions using available data.
method Non-parametric online likelihood-ratio estimation using Pearson-divergence functional minimization.
result The proposed method provides efficient online updates and theoretical guarantees for performance.
Unified view of LR and RP gradients with improved importance sampling.
problem Understanding and optimizing gradient estimators in machine learning.
method First principles explanation of LR and RP, divergence theorem, optimal importance sampling schemes.
result Optimal importance sampling schemes derived with analytic probability densities.
Corrects bias in learned generative models using likelihood-free importance weighting.
problem Bias in learned generative models relative to true data distribution.
method Estimate likelihood ratio using a classifier, apply importance weighting.
result Consistently improves goodness-of-fit metrics for deep generative models.
New method improves likelihood-free parameter estimation in complex models.
problem Estimating parameters in simulation-based models with unknown likelihood.
method Nested multi-time-scale stochastic approximation (NMTS) method.
result Eliminates bias and accelerates convergence in likelihood-free inference.
A pretrained LLM and its finetuned version can detect OOD data effectively.
problem Detecting out-of-distribution data in language models.
method Using the likelihood ratio between a pretrained and finetuned LLM.
result The likelihood ratio is an effective criterion for OOD detection.
Surveying a new method to predict computational hardness in hypothesis testing.
problem Understanding statistical-versus-computational tradeoffs in high-dimensional inference problems.
method The low-degree method, which predicts computational hardness using the second moment of the low-degree likelihood ratio.
result Sharp low-degree lower bounds against subexponential-time algorithms for tensor PCA.
SNVI combines likelihood estimation with variational inference for efficient Bayesian inference.
problem Bayesian inference in models with intractable likelihoods.
method Sequential Neural Variational Inference (SNVI) that combines likelihood-estimation with variational inference.
result SNVI is more computationally efficient than previous algorithms without sacrificing accuracy.
MINIMALIST maximizes mutual information for likelihood estimation from simulated data.
problem Learning model parameters from likelihood functions that cannot be computed.
method Maximizes mutual information between simulated data and model parameters using neural networks.
result Different methods aiming at the same optimal energy form can be directly benchmarked.
This paper develops embeddings that preserve likelihood-based statistical inference.
problem Modern machine learning embeddings destroy the geometric structure required for likelihood-based inference.
method Developed a rigorous theory of likelihood-preserving embeddings and introduced the Likelihood-Ratio Distortion metric.
result Controlling the distortion is necessary and sufficient for preserving inference.
In deep neural network, the cross-entropy loss function is commonly used for classification. Minimizing cross-entropy is equivalent to maximizing likelihood under assumptions of uniform feature and class distributions. It belongs to generative training criteria which does not directly discriminate correct class from co…