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.
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.
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.
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.
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.
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.
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.
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.
Proposes a conservative LR estimator for infrequent data near a frequency threshold.
problem Overestimation of likelihood ratios for infrequent data near a frequency threshold.
method Conservative likelihood ratio estimator for frequencies slightly above a threshold.
result Improves prediction accuracy in named entity context prediction.
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.
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.
Various problems in Engineering and Statistics require the computation of the likelihood ratio function of two probability densities. In classical approaches the two densities are assumed known or to belong to some known parametric family. In a data-driven version we replace this requirement with the availability of da…
Paper tackles moment estimation under covariate shift with a two-stage algorithm.
problem Estimating moments under covariate shift when source and target distributions differ.
method Proposes a two-stage algorithm: first, an optimal estimator for the source distribution; second, likelihood ratio reweighting for calibration.
result Achieves minimax optimal bound for moment estimation.
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.
Sequential hypothesis testing is a desirable decision making strategy in any time sensitive scenario. Compared with fixed sample-size testing, sequential testing is capable of achieving identical probability of error requirements using less samples in average. For a binary detection problem, it is well known that for k…
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.
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.
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.
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.
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.
EQ-Net combines LLR estimation and quantization using deep learning.
problem Unified solution for LLR estimation and quantization.
method Two-stage algorithm using LLR compression as a pretext task.
result Achieves state-of-the-art results with gains in efficiency and latency.
We estimate Radon-Nikodym derivatives using regularization in reproducing kernel Hilbert spaces.
problem Estimating Radon-Nikodym derivatives in various applications.
method General regularization scheme in reproducing kernel Hilbert spaces.
result High order accuracy in reconstructing Radon-Nikodym derivatives at any point.
This paper presents a sequential method to identify the topological ordering of causal DAGs using likelihood ratio scores.
problem Identifying the causal relationships in a data mining scenario with ambiguity of causal directions.
method A general sequential sorting procedure that orders variables one at a time, starting at root nodes, followed by children of the root nodes, and so on until completion. Simple likelihood ratio scores are used to decide the next node to append to the current partial ordering.
result The population version of the procedure provably identifies a true ordering of the underlying DAG under mild assumptions.
New method reduces errors in pricing and sensitivities for discontinuous payoffs.
problem Errors in pricing and sensitivities for discontinuous payoffs in digital and barrier options.
method Alternative methods for estimating sensitivities, including likelihood ratio and hybrid methods.
result New methods substantially reduce test errors in prices and sensitivities.
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.
We introduce a Bayesian solution for the problem in forensic speaker recognition, where there may be very little background material for estimating score calibration parameters. We work within the Bayesian paradigm of evidence reporting and develop a principled probabilistic treatment of the problem, which results in a…
SPRT-TANDEM improves sequential classification accuracy with fewer samples.
problem Efficiently classifying sequential data with high accuracy and low sampling cost.
method Deep neural network-based SPRT algorithm that estimates log-likelihood ratio of two hypotheses.
result SPRT-TANDEM achieves statistically significantly better classification accuracy than other classifiers with fewer samples.
Conditional Leibniz Derivative Estimation reduces variance in stochastic models.
problem Estimating derivatives in stochastic models with discontinuous sample performance.
method Combining push-out likelihood ratio method with Leibniz integral rules.
result Conditional Leibniz estimator reduces variance and is easy to implement.
Near-optimal private tests for simple and MLR hypotheses developed under Gaussian differential privacy.
problem Developing private tests for simple and MLR hypotheses under Gaussian differential privacy.
method A private mean estimator with data-driven clamping bounds, constructing private test statistics.
result Private tests achieve the same asymptotic relative efficiency as non-private most powerful tests.
In this note, we study the relationship between the variational gap and the variance of the (log) likelihood ratio. We show that the gap can be upper bounded by some form of dispersion measure of the likelihood ratio, which suggests the bias of variational inference can be reduced by making the distribution of the like…
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.
We leverage recent breakthroughs in neural density estimation to propose a new unsupervised anomaly detection technique (ANODE). By estimating the probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a likelihood ratio of data vs. background can be co…
Unified framework for ranking-and-selection with multiple correct answers and non-answerable estimates
problem Fixed-precision ranking-and-selection in structured settings with non-unique answers and non-answerable estimates
method Unified framework based on answer-wise acceptance sets, restricted generalized likelihood ratio stopping, and answer-pitfall decomposition
result Unified recipe performs well across a broad range of pure-exploration problems
Financial econometrics has become an increasingly popular research field. In this paper we review a few parametric and nonparametric models and methods used in this area. After introducing several widely used continuous-time and discrete-time models, we study in detail dependence structures of discrete samples, includi…
Reparameterization (RP) and likelihood ratio (LR) gradient estimators are used throughout machine and reinforcement learning; however, they are usually explained as simple mathematical tricks without providing any insight into their nature. We use a first principles approach to explain LR and RP, and show a connection …
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.
This article studies local and global inference for smoothing spline estimation in a unified asymptotic framework. We first introduce a new technical tool called functional Bahadur representation, which significantly generalizes the traditional Bahadur representation in parametric models, that is, Bahadur [Ann. Inst. S…
Paper tackles informative labels in semi-supervised learning, proposing debiasing methods.
problem Informative labels can bias semi-supervised learning models, especially when some classes are more likely to be labeled.
method Estimates missing-data mechanism and uses inverse propensity weighting to debias SSL algorithms.
result Proposed methods improve SSL performance, demonstrated on various datasets including medical ones.
This paper introduces a probability density estimator based on Green's function identities. A density model is constructed under the sole assumption that the probability density is differentiable. The method is implemented as a binary likelihood estimator for classification purposes, so issues such as mis-modeling and …
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.
Hierarchical VAEs detect out-of-distribution data by identifying low-level in-distribution features.
problem Out-of-distribution data often has in-distribution low-level features, leading to misleading likelihood estimates in deep generative models.
method Developed a fast, scalable, unsupervised likelihood-ratio score for out-of-distribution detection based on hierarchical variational autoencoders.
result Achieved state-of-the-art results on out-of-distribution detection across various data and model combinations.
A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions. When the likelihood ratio is unknown, it can b…
In this work, a deep learning-based method for log-likelihood ratio (LLR) lossy compression and quantization is proposed, with emphasis on a single-input single-output uncorrelated fading communication setting. A deep autoencoder network is trained to compress, quantize and reconstruct the bit log-likelihood ratios cor…
Estimates network causal effects considering contagion and latent confounding.
problem Determining if correlations in network studies are due to contagion or latent confounding.
method Segregated graph representation, likelihood ratio tests, network causal effect estimation strategies.
result Proposes methods to estimate network causal effects under full interference scenarios.
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.
Researchers develop methods for inference in hierarchical models using neural simulations.
problem Inference in hierarchical models with intractable likelihoods.
method Construct neural estimators for likelihood-ratio or posterior, accounting for hierarchical structure.
result Explicitly accounting for hierarchical structure leads to tighter parameter constraints.
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 Δn is necessary and sufficient for preserving inference. 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 θ of an underlying theory and measurement apparatus to hig…