Hybrid quantum neural networks predict continuous variables.
problem Predicting continuous variables using quantum computing.
method Quantum classical hybrid neural networks for continuous variable prediction.
result Quantum neural networks outperform classical methods in continuous variable prediction.
Probabilistic inference in graphical models is the task of computing marginal and conditional densities of interest from a factorized representation of a joint probability distribution. Inference algorithms such as variable elimination and belief propagation take advantage of constraints embedded in this factorization …
Safe screening rules reduce ℓ0-regression computation by fixing 76% of variables.
problem Efficiently solving ℓ0-regression problems with large datasets. method Convex relaxation and safe screening rules to eliminate variables.
result 76% of variables can be fixed to their optimal values, reducing computational burden.
Solar algorithm selects variables faster and more accurately in high-dimensional data.
problem Variable selection in high-dimensional data with high accuracy and stability.
method Subsample-ordered least-angle regression (solar) and its coordinate descent generalization (solar-cd) using L0 norm solution path averaging. result Solar selects variables with high accuracy and stability, reducing redundant variable selection.
New method extracts latent variables from process data using autoencoders.
problem Extracting useful information from diverse, noisy, and nonstandard response processes.
method Sequence-to-sequence autoencoder to compress response processes into standard numerical vectors.
result The latent variables extracted from response processes are useful for understanding complex skills.
A new algorithm FastGM speeds up generating Gumbel-Max variables.
problem Efficiently generating multiple Gumbel-Max variables from high-dimensional vectors.
method FastGM reduces time complexity from O(kn+) to O(klnk+n+) by generating variables in descending order. result Significantly reduces computation time for generating k Gumbel-Max variables. We study the computational complexity of Markov chain Monte Carlo (MCMC) methods for high-dimensional Bayesian linear regression under sparsity constraints. We first show that a Bayesian approach can achieve variable-selection consistency under relatively mild conditions on the design matrix. We then demonstrate that t…
New algorithm tackles big data Bayesian problems with latent variables.
problem Bayesian computing for large-scale problems with missing data and dimension jumping.
method Extended stochastic gradient MCMC with latent variables.
result Highly scalable and more efficient than traditional MCMC algorithms.
Bayesian model captures spatial correlations in data.
problem Modeling spatial correlations in high-dimensional data.
method Structured Bayesian Gaussian process latent variable model with parameterized spatial kernel and structure-exploiting algebra.
result Inference is tractable with computational complexity similar to traditional Bayesian GP-LVM.
Framework for completing computational graphs using Gaussian Processes.
problem Completing computational graphs from incomplete data.
method Using Gaussian Processes to approximate unknown functions and recover unobserved variables.
result Efficiently completes computational graphs with fewer data points.
Efficient algorithm approximates discrete random variables with minimal Kolmogorov distance.
problem Estimating the probability of missing deadlines in series-parallel schedules.
method An efficient algorithm that computes a random variable with minimal Kolmogorov distance to a given discrete random variable.
result The algorithm efficiently approximates the probability of missing deadlines with minimal Kolmogorov distance.
Efficient algorithm for Bayesian networks reduces marginal probability distribution computation.
problem Exact computation of marginal probability distribution is NP-hard for categorical variables in Bayesian networks.
method Divide-and-conquer approach exploiting graphical properties of Bayesian networks.
result Novel algorithm outperforms state-of-the-art methods in classification and cancer subtype identification.
Paper calculates optimal use of cheap and expensive data for model accuracy.
problem Optimal design of experiments for variable fidelity data.
method Minimax error analysis for Gaussian process regression.
result Variable fidelity data can improve model accuracy within budget constraints.
New method estimates variable importance for large models efficiently.
problem Estimating variable importance for large, opaque models is computationally challenging.
method Combining early stopping and warm-start techniques for scalable variable importance estimation.
result The method provides theoretical guarantees and demonstrates improved accuracy and computational efficiency.
A flexible variable selection algorithm with sparsistency guarantees.
problem Variable selection in high-dimensional data with theoretical guarantees.
method Kernel-based estimation of regression and gradient functions, followed by hard thresholding.
result Desirable asymptotic sparsistency established for general RKHS.
SVB method provides scalable Bayesian proportional hazards model for high-dimensional gene expression data.
problem Bayesian methods for high-dimensional sparse survival data often sacrifice uncertainty quantification or computational scalability.
method Mean-field variational approximation for scalable Bayesian proportional hazards model.
result SVB method offers posterior distribution for parameters and variable selection via posterior inclusion probabilities.
We introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association among random variables. NMC is defined via an optimization that infers transformations of variables by maximizing aggregate inner products between transformed variables. For finite discrete and jointly Gaussian random vari…
Autoregressive models struggle with hard-to-compute distributions, alternatives like energy-based and latent-variable models solve this.
problem Autoregressive models struggle with distributions whose next-symbol probability is hard to compute.
method Alternatives include energy-based models and latent-variable autoregressive models.
result Alternatives to autoregressive models can escape limitations of hard-to-compute distributions.
Sparse GPs improved with nearest neighbor inducing variables.
problem Sparse GPs struggle with large numbers of inducing variables.
method Introduced a hierarchical prior for inducing variables and used nearest neighbor information for sparsity.
result Significant computational gains compared to standard sparse GPs.
Develops methods to estimate high rank tensors from noisy data.
problem Estimating high rank tensors from noisy observations.
method Generative latent variable tensor model, polynomial-time spectral algorithm.
result Achieves computationally optimal rate for signal tensor estimation.
Two Bayesian optimization methods tackle dynamic design spaces with mixed variables.
problem Optimizing complex systems with varying numbers and types of variables and constraints.
method Two Bayesian optimization approaches: budget allocation and kernel function.
result Both methods converge faster and more consistently than standard approaches.
Improved Shapley Value method for better model interpretation.
problem Misunderstanding and incorrect interpretation of Shapley Values in machine learning models.
method Identification of null and active coalitions, coalitional Shapley Value computation.
result Correct computation and inference of important variables using Shapley Values.
A new method for Bayesian inference using discrete variables.
problem Efficient and exact computations in Bayesian variational inference.
method DIRECT approach exploiting Kronecker matrix algebra for unbiased ELBO gradient estimation.
result Direct models can compute ELBO gradients exactly and use quasi-Newton optimization methods.
Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extr…
Paper generalizes tensor-train approximation for complex random variables.
problem Characterizing intractable high-dimensional random variables.
method Extends inverse Rosenblatt transform to general reference measures and integrates into deep variable transformation framework.
result Deep inverse Rosenblatt transport significantly expands tensor approximations for complex random variables.
Deep learning uses complex networks for high-dimensional data.
problem Computational inefficiency in training deep learning models.
method Use of hierarchical latent variables, efficient linear algebra, SGD optimization, and batch sampling.
result Efficient training and inference possible with optimized algorithms.
Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers for signals observed at constant and variable intervals.
New sampler reduces MCMC complexity for Bayesian variable selection.
problem High-dimensional Bayesian variable selection with high computation complexity.
method Variable-complexity subset weighted-Tempered Gibbs Sampler (wTGS) with Rao-Blackwellized estimator.
result Variances of Rao-Blackwellized estimator are smaller than those of subset wTGS.
D-Wave computers struggle with sampling Boltzmann distributions efficiently.
problem Sampling Boltzmann distributions efficiently on D-Wave computers.
method Exploring various obstacles and remaining difficulties.
result Challenges remain in using D-Wave computers for efficient sampling.
We show that the auxiliary variable method (Møller et al., 2006; Murray et al., 2006) for inference of Markov random fields can be viewed as an approximate Bayesian computation method for likelihood estimation.
Brain signal variability in the measurements obtained from different subjects during different sessions significantly deteriorates the accuracy of most brain-computer interface (BCI) systems. Moreover these variabilities, also known as inter-subject or inter-session variabilities, require lengthy calibration sessions b…
Gaussian process quadrature improves moment transformation accuracy.
problem Computing moments of transformed Gaussian variables with error accounting.
method Bayesian quadrature (Gaussian process quadrature) for numerically estimating integrals.
result Proposed method outperforms classical quadrature methods in accuracy.
Concrete distribution relaxes discrete variables for gradient-based optimization.
problem Gradient-based optimization of discrete random variables.
method Concrete distribution as a continuous relaxation of discrete variables, enabling reparameterization and gradient computation.
result Concrete distribution allows for low-variance biased gradients in discrete stochastic nodes.
New RNN units vary computation to match data flow, improving performance.
problem Fixed computation in RNNs limits model capacity and speed for variable data.
method Modified RNN units learn to vary computation per step.
result Variable computation leads to better performance and fewer operations.
Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Baye…
MCPCA improves data dimensionality by maximizing nonlinear correlations.
problem PCA's limitations in handling nonlinearity and categorical data.
method MCPCA computes nonlinear transformations of variables to maximize covariance matrix Ky Fan norm.
result MCPCA outperforms other methods in dimensionality reduction tasks.
Finding interactions between variables in large and high-dimensional datasets is often a serious computational challenge. Most approaches build up interaction sets incrementally, adding variables in a greedy fashion. The drawback is that potentially informative high-order interactions may be overlooked. Here, we propos…
The paper uses GPR to speed up pricing of GMWB VA with stochastic vol and rate.
problem Pricing and computing Greeks of GMWB VA with stochastic vol and rate.
method Gaussian Process Regression for numerical solution of dynamic control problem.
result GPR significantly speeds up computation with high accuracy.
New algorithm selects relevant variables in high-dimensional graphical models.
problem Automatic selection of relevant variables in high-dimensional graphical models.
method Extends Chow and Liu's algorithm using mutual information and entropy coefficient of determination.
result Outperforms existing methods in selecting variables with explanatory power.
We study statistical calibration, i.e., adjusting features of a computational model that are not observable or controllable in its associated physical system. We focus on functional calibration, which arises in many manufacturing processes where the unobservable features, called calibration variables, are a function of…
Optimal kernel learning improves GP regression for high-dimensional inputs.
problem High computational costs and low prediction accuracy in GP models with many inputs.
method Approximates GP covariance with a convex combination of kernel functions, identifying active variables.
result Improves prediction accuracy and correctly identifies active input variables.
Quantum computing improves copula-based risk aggregation models.
problem Improving quantum models for copula-based risk aggregation.
method Applied Quantum Circuit Born Machine (QCBM) to trapped ion quantum computers, introduced annealing-inspired strategy.
result Quantum models yield comparable or better predictions in risk aggregation tasks.
As inductive inference and machine learning methods in computer science see continued success, researchers are aiming to describe ever more complex probabilistic models and inference algorithms. It is natural to ask whether there is a universal computational procedure for probabilistic inference. We investigate the com…
PEER tackles multi-response regression with incomplete outcomes efficiently.
problem Challenges in estimating, predicting, and computing with large-scale multi-response regression and incomplete outcomes.
method PEER converts multi-response regression into parallel univariate-response regressions.
result PEER achieves consistency in estimation, prediction, and variable selection.
DiCoLa recursively decomposes causal structure learning for latent variables.
problem Learning causal structures in high-dimensional settings with latent variables.
method Recursive decomposition framework for divide-and-conquer causal discovery.
result Theoretical soundness and completeness of DiCoLa framework.
New method identifies latent variables in cognitive models using neural networks.
problem Inference of latent variables in complex cognitive models is limited.
method Recurrent neural networks and simulation-based inference for latent variable sequences.
result Extends neural Bayes estimation to broader classes of cognitive models.
A new screening method for high-dimensional data reduces computational cost.
problem Challenges in variable selection for ultrahigh-dimensional linear regression.
method Ordering absolute sample ridge partial correlations to screen variables.
result The method provides sure screening property without strong assumptions.
Improved Lasso estimator speeds up variable selection.
problem Efficient variable selection in high-dimensional data.
method Stability principle-based generalized debiased Lasso.
result Significantly reduces computational cost of resampling-based methods.