Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model …
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48 results for “Non-Parametric Models”
Proposes method for eliciting non-parametric joint priors using normalizing flows.
problem Learning complex non-parametric joint priors for model parameters.
method Expert elicitation combined with normalizing flows for generative modeling.
result Framework supports elicitation of both parametric and non-parametric priors.
Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture modeling, where the number of mixture components grows with the number of data items. The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped data, often used for non-parametric topic modeling, where each group is a mixtur…
New graphical criteria for efficient covariate adjustment in non-parametric causal models.
problem Estimating population average treatment effects in observational studies using non-parametric causal graphical models.
method Developed new graphical criteria to determine efficient covariate adjustment sets for estimating treatment effects in non-parametric causal graphical models.
result Graphical criteria for efficient covariate adjustment can be applied in both linear and non-parametric causal models.
NPOD algorithm improves efficiency in estimating pharmacokinetic parameters.
problem Efficiently estimating joint distribution of model parameters in population pharmacokinetics.
method Uses gradient approach to suggest new support points, reducing evaluation time.
result Achieves similar solutions to NPAG but with significantly fewer cycles and runtime.
Develops flexible non-parametric ACFs using B-spline kernels.
problem Flexible modelling of the autocovariance function (ACF) in time-series, spatial, and spatio-temporal analysis.
method Derives the inverse Fourier transform of B-spline spectral bases to create a general class of non-parametric ACFs.
result Provides a provably dense, flexible, and general class of non-parametric ACFs for various types of processes.
Study provides guarantees for kernel clustering under non-parametric mixtures.
problem Statistical guarantees for kernel-based clustering without strong assumptions.
method Non-parametric mixture models, kernel-based clustering, consistency guarantees.
result Necessary and sufficient separability conditions for consistent clustering recovery.
A novel MCMC method clusters data faster and more accurately.
problem Efficiently clustering large datasets with unknown number of clusters.
method Master/Worker architecture for distributed MCMC inference.
result Significant improvement in clustering accuracy and speed.
Estimates non-parametric logistic model using case-control data and external summary info.
problem Imbalanced binary data in case-control studies.
method Two-step estimation procedure with deep neural network for functional approximation.
result Proposed estimator achieves optimal convergence rate in non-parametric regression.
Study evaluates policies in partially observable environments without full model specification.
problem Evaluating policies in partially observable environments without full model specification.
method Developed non-parametric identification and recursive fitted-Q-evaluation algorithm.
result Established finite-sample error bounds for policy value estimation.
Develops a new method for learning non-parametric DAGs using RKHS.
problem Challenges of learning non-parametric causal models with large combinatorial search space.
method Uses reproducing kernel Hilbert spaces (RKHS) and sparsity-inducing regularization terms based on partial derivatives to enforce acyclicity.
result Shows improved performance through simulations and data analyses.
New network learns non-parametric invariances from data.
problem Modeling non-parametric invariances in data.
method Introduces PRC-NPTN networks with permanent random connectomes.
result Improves generalization and outperforms existing methods.
FAMOS combines parametric and non-parametric methods for efficient image stylization.
problem Efficiently stylize images with limited data and compute resources.
method Fully Adversarial Mosaics (FAMOS) that integrates parametric and non-parametric approaches.
result Demonstrates the effectiveness of FAMOS in stylizing images with minimal data and compute resources.
New method measures model risk in dynamic settings with uncertain state processes.
problem Lack of non-parametric approach for dynamic model risk quantification.
method Generalizes relative-entropic approach to dynamic case under -divergence.
result Unified treatment for worst-case risk and -divergence budget.
Non-parametric time series forecasting without assuming a specific distribution.
problem Time series forecasting with numerical stability issues in classical models.
method Generates predictions by sampling from the empirical distribution of time series data.
result The proposed method produces reasonable forecasts without numerical stability issues.
Bayesian model learns complex multivariate dependencies.
problem Learning dependency structures across multiple dimensions.
method Flexible Gaussian process priors and Dirichlet process for structure learning.
result Efficient variational inference for model parameters.
Enhances ML model explanations through Bayesian non-parametric approach.
problem Difficulty in understanding and interpreting ML model decisions.
method Augments a Bayesian non-parametric regression mixture model with elastic nets.
result Empirically outperforms state-of-the-art techniques in explaining individual decisions.
The paper reviews methods for estimating individual treatment effects using non-parametric regression models.
problem Estimating heterogeneous treatment effects in observational data.
method Non-parametric regression models to estimate individual treatment effects.
result A review and development of existing state-of-the-art frameworks for individual treatment effects estimation.
Bayesian econometrics improves nowcasting during pandemics.
problem Improving nowcasting during extreme economic events like pandemics.
method Bayesian econometric methods using non-parametric mixed frequency VARs with additive regression trees.
result Significant improvements in nowcasting performance compared to linear models.
New test detects when generative models memorize training data.
problem Detecting when generative models overfit by memorizing training data.
method A non-parametric three-sample test using training set, target distribution, and model-generated samples.
result The test effectively detects data-copying in various models and datasets.
Non-parametric estimators improve quickest changepoint detection under irregular sequence lengths.
problem Limited and irregular sequence lengths hinder application of ARL and ADD in QCD.
method Analogies with survival analysis to model detection probabilities under truncation.
result KM-ARL and KM-ADD non-parametric estimators are asymptotically unbiased.
New defense method for non-parametric classifiers robust against adversarial attacks.
problem Lack of robustness in non-parametric classifiers against adversarial attacks.
method Adversarial pruning method to preprocess datasets and a novel attack.
result Adversarial pruning provides a robust defense for non-parametric classifiers.
Paper develops a non-parametric model to estimate influence networks in high-dimensional time series.
problem Estimating influence networks in high-dimensional time series with many variables.
method Non-parametric sparse additive model (SpAM) using β and φ-mixing properties of Markov chains and empirical process techniques for RKHSs.
result Sharp upper bounds on mean-squared error for estimating influence networks.
Study compares non-parametric models for predicting medical insurance reimbursement delays.
problem Estimating the time-lapse between medical insurance reimbursement.
method Comparative study of four non-parametric regression models (KNNs, SVMs, Decision Trees, Random Forests) using R-squared metric.
result Each model's performance varies with training data size, feature space, and hyperparameters.
Tests for equivariance in non-parametric regression models.
problem Detecting false assumptions of symmetry in regression models.
method Develops tests for -equivariance independent of the model.
result Confidence in using equivariant models when symmetry is unknown.
The paper develops a theory for identifying the best arm in non-parametric multi-armed bandits with a fixed budget.
problem Identifying the best arm in non-parametric multi-armed bandits with a limited number of trials.
method The paper proposes upper and lower bounds on the average log-probability of misidentification using information-theoretic quantities and a refined analysis of the successive-rejects strategy.
result The paper provides new upper and lower bounds on the average log-probability of misidentification, which generalize existing bounds.
A new statistical model uses Orlicz-Sobolev spaces with Gaussian weight.
problem Statistical modeling of infinite-dimensional probability measures.
method Affine statistical bundle on Gaussian Orlicz-Sobolev space.
result Provides tools for solving infinite-dimensional evolution problems.
The paper provides theoretical guarantees for transformation-based models in variational inference.
problem Theoretical justification for transformation-based models in variational inference.
method Theoretical analysis of non-linear latent variable models and Gaussian process priors.
result Theoretical guarantees for implicit variational inference, achieving optimal risk bounds and approximating the true posterior.
This paper improves inference for non-parametric Bayesian Hawkes processes.
problem Inference of non-parametric Bayesian Hawkes processes is unscalable or slow.
method Proposes a squared sparse Gaussian process for the triggering kernel and a novel variational inference schema.
result The method accelerates inference to linear time complexity and improves model selection.
Occam's Ghoststat.ML
The article applies Occam's Razor to non-parametric model building, minimizing the number of bits for data encoding.
problem Overlooking the role of model parameters in data encoding leads to inefficient probability density estimators.
method Extends bit counting to model parameters, providing a true measure of complexity for parametric models.
result Minimizing total bit requirement leads to smoother, more efficient probability density estimates and fewer relevant parameters.
Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Explorationmath.ST
Estimates neural drift for stochastic equations, improving inference on noisy data.
problem Estimating drift in stochastic differential equations with neural networks.
method Non-parametric estimation using ReLU neural networks, enforcing theoretical bounds.
result Practical method for inference on noisy and rough functional data.
DPPS uses DP priors for Bayesian non-parametric multi-arm bandits.
problem Optimizing multi-arm bandit environments with prior beliefs.
method Bayesian non-parametric algorithm based on Dirichlet Process priors.
result DPPS provides principled incorporation of prior beliefs and is optimal in Bayesian regret setup.
Paper proposes a neural network for non-parametric Hawkes process kernel estimation.
problem Estimating non-parametric Hawkes process kernels efficiently and interpretably.
method Single hidden layer neural network for unbiased log-likelihood estimation of Hawkes processes.
result Proposed neural network achieves comparable or better performance than existing methods.
Proposes a non-parametric method for deep discrete latent variable models.
problem Learning sparse discrete latent representations in deep models.
method Iterative algorithm with Beta-Bernoulli process prior and local data scaling.
result Improves sparsity and scalability of deep discrete latent variable models.
iCOS: Option-Implied COS Methodq-fin.ST
iCOS method estimates risk-neutral densities and option prices without model assumptions.
problem Estimating risk-neutral densities and option prices without model assumptions.
method Leverages Fourier-cosine technique using option-implied cosine series coefficients, without model assumptions.
result Effective in extracting information from option prices under various market conditions.
This article reviews the Author-Topic Model and presents a new non-parametric extension based on the Hierarchical Dirichlet Process. The extension is especially suitable when no prior information about the number of components necessary is available. A blocked Gibbs sampler is described and focus put on staying as clos…
The paper calibrates probabilistic regression models to better estimate real-valued targets.
problem Improving probability density estimates for regression models.
method Proposes three non-parametric approaches to calibrate regression models.
result Demonstrates improved performance of regression models on predictive likelihood.
Bayesian non-parametric model adapts to concept drifts in streaming data.
problem Inference under concept drift phenomenon for non-stationary data streams.
method Variational inference algorithm for Dirichlet process mixture models with exponential forgetting.
result The proposed model outperforms state-of-the-art algorithms in clustering problems.
ELNN uses neural networks for improved option pricing.
problem Inconsistent pricing of over-the-counter products and unacceptable outcomes in ANN-based models.
method ELNN integrates ANNs with the exponential Levy model, addressing issues with existing models.
result ELNN outperforms Merton and Kou models in fitting performance and stability of estimates.
This study examines when non-parametric methods are robust to adversarial examples.
problem Understanding when non-parametric methods are robust to adversarial examples.
method Examined general non-parametric methods and established conditions for r-consistency.
result Non-parametric methods like nearest neighbors and kernel classifiers are r-consistent when data is well-separated, while histograms are not.
In this paper, we propose a non-parametric conditional factor regression (NCFR)model for domains with high-dimensional input and response. NCFR enhances linear regression in two ways: a) introducing low-dimensional latent factors leading to dimensionality reduction and b) integrating an Indian Buffet Process as a prior…
The CAPM fails to explain small firm effect and proposes semi-parametric measures.
problem The CAPM fails to explain the small firm effect and is biased and inconsistent.
method Uses non-parametric and semi-parametric asset pricing models to analyze risk and performance measures.
result Semi-parametric measures are non-constant under extreme market conditions and not significantly different from the Fama-French three-factor model.
Enhanced GCNs with non-parametric activation functions outperform baselines.
problem Simple activation functions in GNNs limit model flexibility.
method Extended kernel activation function for GCNs, regularizable and smooth.
result Significant improvement over baseline GCNs with similar depth/size.
Improves NMT performance on diverse datasets without forgetting.
problem Catastrophic forgetting in NMT models on heterogeneous datasets.
method Non-parametric n-gram level retrieval combined with expressive neural network.
result Gains on all evaluation sets on a heterogeneous dataset.
BN^2MF identifies unknown exposure patterns in environmental mixtures.
problem Identifying unknown exposure patterns in environmental mixtures.
method Bayesian non-parametric non-negative matrix factorization (BN^2MF) with non-negative continuous priors and a non-parametric sparse prior.
result Estimates patterns of chemical exposures without specifying the number of patterns.
The paper improves prediction intervals for non-parametric regression using histograms.
problem Computing accurate prediction intervals for non-parametric regression models.
method Uses conditional histograms to estimate conditional distributions and compute shortest prediction intervals.
result The method provides prediction intervals with provable marginal coverage and asymptotic conditional coverage.
Paper proposes method to detect dependencies in large, sparse datasets.
problem Detecting true predictive relationships in large, sparse datasets with missing values.
method Combines probabilistic programming, information theory, and non-parametric Bayes.
result Shows improved sensitivity and specificity over baselines from statistics and machine learning.
Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.
problem Over-engineering in GNN architectures for common semi-supervised node classification datasets.
method Replacing feature aggregation with a non-parametric learner to streamline GNN design.
result Non-parametric regression is effective for semi-supervised learning on sparse, directed networks.