We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variable…
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48 results for “probabilistic estimation”
Transformers interpret as probabilistic mixtures, offering new insights.
problem Understanding Transformers from a probabilistic perspective.
method Modeling Transformers as mixtures of Gaussian models.
result Transformers can be seen as maximum posterior probability estimators.
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimator…
MPF method improves parameter estimation in probabilistic models.
problem Difficulty in fitting probabilistic models due to intractable partition function.
method Minimum Probability Flow (MPF) method for parameter estimation.
result MPF outperforms existing techniques in convergence time and accuracy.
DALTON improves ODE parameter estimation by learning from noisy data.
problem High sensitivity to parameters in ODEs produces unreliable parameter estimates.
method Data-adaptive probabilistic likelihood approximation for ODEs.
result DALTON produces more accurate parameter estimates than existing methods.
Calibrated probabilistic solvers improve accuracy of ODE estimates.
problem Uncertainty in probabilistic ODE solutions is not well-calibrated for adaptive step sizes.
method Introduce and assess several calibration methods for probabilistic ODE solvers.
result Calibration methods interact efficiently with adaptive step-size selection, improving posteriors.
A new estimator improves training of probabilistic models with latent Gaussian variables.
problem Improving gradient estimation for models with latent Gaussian variables.
method Rao-Blackwellised Reparameterisation Gradients (R2-G2)
result R2-G2 consistently yields better performance in models with multiple applications of the reparameterisation trick.
Paper uses GMM and MAF for probabilistic classification, outperforming simpler models.
problem Classifying data with complex distributions.
method Density estimation using Gaussian Mixture Model and Masked Autoregressive Flow.
result Proposed classifiers outperform simpler models like linear discriminant analysis.
Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insightsstat.ML
Paper quantifies uncertainty in probabilistic models using Gaussian Processes.
problem Assessing reliability of probabilistic machine learning predictions.
method Systematic framework for estimating epistemic and aleatoric uncertainty, using Gaussian Processes and Monte Carlo sampling.
result Effective approach for quantifying prediction confidence in probabilistic models.
Kernel quadrature improves CRPS estimation for probabilistic time-series forecasting.
problem Intractable integrations in CRPS evaluation metrics lead to improper rankings of forecasting models.
method Introduced kernel quadrature approach for unbiased CRPS estimation and scalable computation.
result Our approach consistently outperforms existing CRPS estimators.
PGBM creates probabilistic predictions efficiently.
problem Creating probabilistic predictions for large-scale data.
method Approximates leaf weights as random variables, learns moments via stochastic tree ensemble update equations.
result PGBM offers significant speedup and accuracy improvements over existing methods.
Estimates chirp signal frequencies using probabilistic models.
problem Estimating instantaneous frequencies of chirp signals when true forms are unknown.
method Non-linear Gaussian processes and stochastic filters/smothers for posterior estimation.
result The method outperforms state-of-the-art methods on synthetic and real-world datasets.
A new probabilistic mixup framework improves deep learning generalization.
problem Improving deep learning generalization performance.
method Introduces a novel probabilistic mixup framework for conditional density estimation.
result Empirical results show benefits over existing mixup variants.
Probabilistic method proves gap estimates on sphere.
problem Proving fundamental gap estimates for Schrödinger operators on spheres.
method Reflection coupling method on Riemannian manifolds.
result Extends probabilistic proof to sphere, generalizing previous results.
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. In this paper we de…
EASE estimator improves probabilistic value estimation efficiency.
problem Efficiently estimating probabilistic values like Shapley and semivalues.
method Developed an Efficiency-Aware Surrogate-adjusted Estimator (EASE) that minimizes first-order mean squared error.
result EASE consistently outperforms existing estimators for various probabilistic values.
New approach combines geometric and probabilistic methods to estimate manifold dimension in high-dimensional data.
problem Estimating the dimension of manifolds in high-dimensional data.
method Combines a modified box-counting algorithm (geometric) and a new probabilistic method (nearest neighbor distance analysis).
result The combined method is robust, fast, and effective in estimating manifold dimension.
This paper improves DNN generalization by accurately estimating mutual information.
problem Intractability of estimating mutual information in DNNs.
method Introduces a probabilistic representation of DNNs to accurately estimate mutual information.
result Derives a tighter generalization bound than previous relaxations.
Develops effective adversarial attacks on probabilistic forecasting models.
problem Adversarial attacks on neural models outputting probability distributions.
method Effective generation of adversarial attacks through Monte-Carlo estimation and Bayesian conditioning.
result Demonstrates successful generation of attacks with small input perturbations.
ProbETA models travel time correlations between trips for better navigation.
problem Trip correlations not captured by existing methods.
method Deep hierarchical joint probabilistic model with learnable link representations.
result ProbETA outperforms state-of-the-art methods with 12.60% MAPE reduction.
VIR model improves regression accuracy and uncertainty estimation for imbalanced data.
problem Imbalanced regression datasets lead to poor model accuracy and uncertainty estimation.
method VIR model uses probabilistic smoothing and reweighting to estimate latent representations and uncertainty.
result VIR model outperforms state-of-the-art models in accuracy and uncertainty estimation.
A deep learning method for probabilistic weather forecasting.
problem Probabilistic forecasting of weather.
method Two chained machine-learning steps: dimension reduction and density estimation using normalizing flows.
result The method produces accurate conditional forecast distributions for weather.
This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively …
The paper investigates model collapse in language models from a probabilistic perspective.
problem Understanding and preventing model collapse in language model training.
method Investigates recursive parametric model training from a probabilistic standpoint, characterizing conditions for model collapse and proposing mitigation strategies.
result Progressively increasing sample size is necessary to prevent model collapse, with a superlinear growth rate required in the asymptotic regime.
dynestyx: A library for probabilistic programming of dynamical systems
problem integrating state-space models into probabilistic programming languages
method a unified interface for specifying priors and performing inference
result principled uncertainty quantification for state and parameters
Study proposes new methods to calculate probabilistic benchmarks in noisy data.
problem Identifying opportunities for improvement in comparable units with noisy data.
method 2-step methodology involving undersampling and relevance vector machine.
result Higher discrimination power achieved with macro-economic environment variables.
We present Natural Gradient Boosting (NGBoost), an algorithm for generic probabilistic prediction via gradient boosting. Typical regression models return a point estimate, conditional on covariates, but probabilistic regression models output a full probability distribution over the outcome space, conditional on the cov…
PRISM provides real-time SLAM with uncertainty estimates for agent and map states.
problem Lack of uncertainty estimates and real-time capability in SLAM.
method Combines differentiable rendering and 6-DoF dynamics, uses approximations for Bayesian inference.
result Runs at 10Hz real-time with similar accuracy to state-of-the-art SLAM.
Simplifies efficient estimation via automatic differentiation and probabilistic programming.
problem Constructing efficient estimators for complex statistical models.
method Automatic differentiation applied to statistical functionals, avoiding the need to derive efficient influence functions.
result Users can generate efficient estimators with minimal code, simplifying the process for non-experts.
Probabilistic learning for binary classification with categorical variables.
problem Binary classification with categorical covariates.
method Probabilistic analysis and two algorithms for learning boolean functions.
result Effective learning of boolean functions from binary data.
Paper presents estimators for entropy and information in probabilistic models.
problem Estimating entropy and mutual information in high dimensions is challenging.
method EEVI uses importance sampling with proposal distributions like amortized variational inference and sequential Monte Carlo.
result EEVI delivers accurate upper and lower bounds on information quantities.
Partition Tree estimates conditional densities for mixed continuous and categorical variables.
problem Estimating conditional densities for mixed data types.
method Tree-based framework modeling conditional distributions as piecewise-constant densities on adaptive partitions, minimizing conditional negative log-likelihood.
result Improved probabilistic prediction compared to CART-style trees and state-of-the-art methods.
Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable. Unfortunately, Bayesian parameter estimation for highly coupled models such as regressions and state-space models still scales poorly; each MCMC transition takes linear time i…
We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…
CCs learn high-dimensional distributions from heterogeneous data.
problem Learning high-dimensional distributions from heterogeneous data.
method Introducing characteristic circuits (CCs) that learn from data and use spectral domain.
result CCs outperform state-of-the-art density estimators on common benchmark data sets.
Proposes a cost-sensitive method to generate probabilistic SVM outputs.
problem Generating probabilistic SVM outputs efficiently and cost-effectively.
method Cost-sensitive ensemble SVM with bootstrap probability estimation.
result Improves performance on imbalanced datasets and outperforms benchmarks.
Paper improves uncertainty estimation in LLM-as-a-judge systems.
problem Improving uncertainty estimation in LLM-as-a-judge frameworks.
method Generalised probabilistic modelling and improved uncertainty estimates.
result Proposed uncertainty estimates significantly improve system efficiency.
Paper introduces probabilistic forecasting methods for cryptocurrency volatility.
problem Inadequate point forecasting methods for capturing full spectrum of volatility outcomes.
method Combines multiple base models (statistical and machine learning) to estimate conditional quantiles of cryptocurrency realized variance.
result QRS method outperforms sophisticated alternatives for Bitcoin volatility forecasting.
New probabilistic method speeds up calibration of complex models.
problem Calibrating large-scale differential equation models efficiently.
method Probabilistic approach to computing local sensitivities.
result Significantly reduces computational effort for iterative gradient-based calibration.
Paper proposes a probabilistic method to handle missing data in decision trees.
problem Handling missing data in decision trees.
method At deployment time, use density estimators to compute expected predictions. At learning time, fine-tune tree parameters to minimize expected prediction loss.
result Effective compared to baselines in experiments.
New solver avoids memory issues for long differential equations.
problem Memory constraints in adaptive probabilistic ODE solvers.
method Fixed memory demands adaptive probabilistic solver using robust state estimation.
result Eliminates memory issues for long time series simulations.
Study introduces a probabilistic framework for air-sea fluxes using neural networks.
problem Accurately quantifying air-sea fluxes for understanding interactions and improving weather/climate models.
method Gaussian distributions conditioned on input variables, artificial neural networks, eddy-covariance data, minimizing negative log-likelihood loss.
result Trained neural networks provide alternative mean flux estimates and quantify uncertainty.
In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show that Batch Normalization maximazes the lower bound of its marginalized log-likelihood. Then, according to the new probabilistic model, we design an algorithm which acts cons…
Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) algorithms in existing probabilistic programming systems suboptimally use only model priors as proposal distributions. In this work, we describe an approach for training a discriminative model, namely a neural network, in order to approximate the …
In general, gradient estimates are very important and necessary for deriving convergence results in different geometric flows, and most of them are obtained by analytic methods. In this paper, we will apply a stochastic approach to systematically give gradient estimates for some important geometric quantities under the…
This manuscript proposes a probabilistic framework for algorithms that iteratively solve unconstrained linear problems with positive definite for . The goal is to replace the point estimates returned by existing methods with a Gaussian posterior belief over the elements of the inverse of , which can …
Deep state space model forecasts time series with uncertainty.
problem Probabilistic forecasting for risk management.
method Parameterized deep networks for non-linear models, recurrent neural nets for dependency, ARD network for exogenous variables.
result Accurate and sharp probabilistic forecasts with realistic uncertainty growth.
Inferring Microbial Biomass Yield and Cell Weight using Probabilistic Macrochemical Modelingq-bio.QM
Method improves microbial biomass yield estimation from noisy data.
problem Estimating microbial biomass yields from noisy cell counts and substrate measurements.
method Probabilistic macrochemical modeling to relax cell weight assumptions and improve robustness.
result Model provides accurate uncertainty estimates of key parameters.