Develops inference combinators for probabilistic programs using neural networks.
problem Creating efficient proposals for probabilistic program inference.
method Inference combinators using neural network parameterization of proposals.
result Correct by construction variational methods tailored to specific models.
Probabilistic programs with dynamic computation graphs can define measures over sample spaces with unbounded dimensionality, which constitute programmatic analogues to Bayesian nonparametrics. Owing to the generality of this model class, inference relies on `black-box' Monte Carlo methods that are often not able to tak…
Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabil…
ParKCa combines multiple causal inference methods to infer new causes from known and unknown factors.
problem Causal inference from observational data when randomized experiments are not feasible.
method ParKCa uses a stacking approach to combine results from multiple causal inference methods.
result ParKCa infers more causes than existing methods in real-world and simulated datasets.
NCoRE learns counterfactual representations for combined treatments.
problem Estimating individual response to multiple simultaneous interventions.
method Neural conditional representation with modulators for cross-treatment interactions.
result NCoRE significantly outperforms existing methods in counterfactual treatment effect estimation.
Adaptive workflow combines fast amortized inference with MCMC for many datasets.
problem Trade-off between computational speed and sampling accuracy in Bayesian inference.
method Adaptive workflow integrating amortized inference and MCMC with principled diagnostics.
result Efficiency gains with high posterior quality on tens of thousands of datasets.
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.
Paper combines regularization and pruning to reduce FLOPs in DNNs.
problem Efficient inference in deep learning models.
method Apply mixup and cutout regularizations and soft filter pruning to the ResNet architecture.
result Combining regularization and pruning reduces FLOPs more effectively than each technique alone.
An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well as over the choice of tractable families used for the variational approximation. In this paper, we present a novel combination of graph part…
Variational inference is increasingly being addressed with stochastic optimization. In this setting, the gradient's variance plays a crucial role in the optimization procedure, since high variance gradients lead to poor convergence. A popular approach used to reduce gradient's variance involves the use of control varia…
New method combines ODE solvers with Bayesian inference for efficient model training.
problem Combining ODE solvers with Bayesian inference for efficient model training.
method Probabilistic state space model using extended Kalman filter for joint inference from differential equations and data.
result Efficient approximate Bayesian inference on latent force and ODE solution.
CoSMIC extends flow-based SVI to transdimensional problems.
problem Bayesian structure learning and model selection with multi-model parameter spaces.
method Normalizing flows with a combined stochastic variational transdimensional inference approach.
result Improved performance on high-cardinality model spaces.
A new method combines scores of individual observations to efficiently approximate posterior distributions.
problem Handling posterior distributions conditioned on multiple observations with neural methods.
method Conditional score modeling to combine learned scores from individual observations.
result Sample-efficient method that can aggregate multiple observations at inference time.
Unified framework DDNs for multi-label classification, improving inference efficiency.
problem Efficient inference for multi-label classification with dependency networks.
method Combining dependency networks and deep learning, proposing novel inference schemes.
result Novel inference schemes outperform basic neural architectures and Markov networks.
Gradient-guided nested sampling improves posterior inference efficiency.
problem Efficiently sampling from complex posterior distributions.
method Gradient-guided nested sampling combining differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization.
result Significantly faster mode discovery and more accurate partition function estimates.
Bayesian Federated Inference combines local data analyses to estimate regression models.
problem Estimating accurate parameters with limited data from different centers.
method Bayesian Federated Inference (BFI) for pooling local data analyses.
result Excellent performance of BFI methodology shown in real-life examples.
Combines structured inference and targeted learning to tackle causal inference challenges.
problem Treatment assignment heterogeneity and lack of counterfactual data.
method Factorizes joint distribution into risk, confounding, instrumental, and miscellaneous factors; applies regularizer derived from influence curve.
result TVAE demonstrates competitive and state-of-the-art performance on benchmark datasets.
New method combines experimental and observational data for causal inference.
problem Combining internal validity of experiments and larger sample sizes of observations.
method Empirical risk minimization (ERM) framework with cross-validation.
result Efficacy and reliability demonstrated on real and synthetic data.
For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e.g., by looking at and touching objects. Despite its importance, multisensory 3D scene representation learning has received less attention compared to t…
Generative Augmented Inference improves AI-generated data for causal inference.
problem Challenges in using AI-generated annotations for reliable causal inference.
method Generative Augmented Inference (GAI) treats AI outputs as informative features for learning true labels, flexibly modeling the relationship using nonparametric methods.
result GAI significantly reduces estimation error and improves confidence interval quality compared to human-only and PPI-based methods.
New method for inferring network topology from partial data.
problem Inferring network topology from limited node data.
method Vector autoregressive model and Gaussian mixture algorithm.
result The proposed method converges to the network combination matrix in probability.
GWI combines deep neural networks with Gaussian processes for better predictive performance and uncertainty quantification.
problem Combining deep learning with Gaussian process uncertainty quantification.
method Gaussian Wasserstein inference (GWI) using Wasserstein distance between Gaussian measures.
result GWI achieves state-of-the-art performance on benchmark datasets.
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing inferences from these models, but doing this successfully is severely hampered by the wide range of non--standard models they can express. As …
Synthetic Combinations learns unit-specific causal outcomes for combinatorial interventions.
problem Estimating unit-specific causal outcomes for all combinations of p interventions in a heterogeneous setting. method Latent factor model with Fourier expansion sparsity, imposing structure across units and interventions.
result Synthetic Combinations provides consistent estimation with poly(r) * (N + s^2p) observations, outperforming previous methods.
New methods combine model predictions to avoid linear mixtures' limitations.
problem Combining predictions from different models to avoid linear mixtures' limitations.
method Log-linear pooling (locking) and quantum superposition (quacking) to optimise model weights.
result Demonstrated locking method with illustrative example and practical application.
EnKO combines VI and EnKF for efficient latent dynamics inference.
problem Particle degeneracy and biased gradient estimators in SMC-based methods.
method EnKO: hybrid of VI and EnKF.
result EnKO outperforms SMC-based methods in predictive ability and particle efficiency.
Novel framework combines tree-based discretization and ILP matching for causal inference.
problem Challenges in identifying causal relationships from observational data.
method Combines tree-based discretization and ILP matching for causal inference.
result Yields computational efficiency and less biased ATT estimates.
Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationship…
Long Short-Term Memory (LSTM) is one of the most powerful sequence models. Despite the strong performance, however, it lacks the nice interpretability as in state space models. In this paper, we present a way to combine the best of both worlds by introducing State Space LSTM (SSL) models that generalizes the earlier wo…
A new method combines VI and IS to improve Bayesian inference accuracy.
problem Bayesian inference often underestimates posterior tails, leading to miscalibration and degeneracy.
method Proposes a novel combination of optimization and sampling techniques using the forward KL divergence.
result The method guarantees asymptotic consistency and fast convergence to optimal IS and variational approximations.
A new method combines variational inference and MCMC for efficient data subsampling.
problem Combining variational inference and MCMC for data subsampling.
method Introduces a surrogate likelihood to learn jointly with variational parameters.
result Permits an intuitive trade-off between inference fidelity and computational cost.
A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical model. However, in many cases the generating process is only a poor approximation of the much more complex true data generating process, leadi…
We develop a method to combine Markov chain Monte Carlo (MCMC) and variational inference (VI), leveraging the advantages of both inference approaches. Specifically, we improve the variational distribution by running a few MCMC steps. To make inference tractable, we introduce the variational contrastive divergence (VCD)…
DECI combines causal discovery and inference in a single model for diverse data types.
problem Combining causal discovery and inference methods for diverse data types.
method Develops a single flow-based non-linear additive noise model (DECI) for causal discovery and inference.
result DECI can recover ground truth causal graphs and perform (C)ATE estimation.
We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational infe…
A new method combines MCMC results to avoid failures in parallel computing.
problem Parallel MCMC's sensitivity to subposterior sampling issues leads to failures.
method Parallel Active Inference (PAI) uses Gaussian Process (GP) surrogate modeling and active learning.
result PAI successfully combines MCMC results where previous methods fail.
FTIP uses normalizing flows to improve posterior inference in function space.
problem Challenges in posterior inference with implicit-process priors.
method FTIP uses normalizing flows to define a richer variational distribution over combination weights.
result FTIP captures asymmetric and multimodal posterior structure better than Gaussian coefficient approximations.
New method combines HQR and WACI for better time series prediction intervals.
problem Challenges in creating reliable prediction intervals for time series forecasting.
method Combining Heteroscedastic Quantile Regression (HQR) with Width-Adaptive Conformal Inference (WACI).
result Combined approach meets or surpasses typical benchmarks for validity and efficiency.
Energy-based models can generate complex images by combining simpler concepts.
problem Generating natural images that satisfy complex logical combinations of concepts.
method Energy-based models combine probability distributions of simpler concepts to generate compositions.
result Energy-based models can generate images that satisfy conjunctions, disjunctions, and negations of concepts.
XSPNs combine SPNs and MEVMs for efficient inference in data with repeated parts.
problem Efficient inference in data with repeated interchangeable parts.
method Introducing Exchangeability-Aware Sum-Product Networks (XSPNs) that combine SPNs and MEVMs.
result XSPNs can be more accurate than conventional SPNs when data contains repeated parts.
Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational message-passing algorithm for variational inference in such models. We make three contributions. First, we propose structured inference network…
Matched Machine Learning combines machine learning and matching for causal inference.
problem Non-interpretable methods for causal inference.
method Combines machine learning and matching for interpretable causal inference.
result Performs as well as black-box machine learning methods and better than existing matching methods.
Combines neural networks with variational inference for better uncertainty quantification.
problem Overconfident predictions from traditional neural networks and time-consuming Bayesian optimization.
method VIFO (Variational Inference on the Final-Layer Output) using neural networks to learn mean and variance.
result VIFO provides a good tradeoff in run time and uncertainty quantification, especially for out of distribution data.
Study uses Wasserstein distance to identify causal orders and unmix sources.
problem Identifying causal relationships and separating sources in non-Gaussian data.
method Wasserstein distance for non-Gaussianity, linear ICA, causal inference.
result Exact identification of ICA unmixing matrix and causal orders.
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
We provide the first information theoretic tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities. Our work bridges recent theoretical breakthroughs in the detection of latent community structure without no…
The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniqu…
New methods for scalable inference in modular models with misspecified sub-models.
problem Model misspecification in multi-modular models complicates evidence combination.
method Variational methods for approximating Cut and SMI posteriors, and Variational Meta-Posterior.
result Feasibility of analysis with multiple cuts using a single set of variational parameters.