arXiv research

Smooth Schrödinger Bridges improve trajectory inference by smoothing Gaussian processes.

SIXO improves inference by learning smoothing distributions from all observations.

SPH-ParVI uses fluid dynamics to sample unknown densities efficiently.

Algorithm improves variational inference in Wasserstein distance.

Efficient EP algorithm improves smoothing distribution inference in financial models.

NAS-X improves inference and model learning for SLVMs.

New method improves counterfactual distribution learning for high-dimensional outcomes.

Infer-AVAE infers missing user attributes from incomplete data using a novel adversarial approach.

New method for adaptive estimation and inference in econometric models without knowing smoothness.

Improved state estimation in nonlinear models using amortized backward variational inference.

Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP inference entails solving a combinatorial optimization problem to find the most likely configuration of the discrete-valued model. Linear programming (LP) relaxations in the Sh…

Unified framework for efficient Gaussian process inference.

New metric improves latent dynamics inference from neural data.

Kalman filtering and smoothing algorithms are used in many areas, including tracking and navigation, medical applications, and financial trend filtering. One of the basic assumptions required to apply the Kalman smoothing framework is that error covariance matrices are known and given. In this paper, we study a general…

Improved estimators for causal inference using cross-fitting and undersmoothing.

Estimates individual treatment effects using gradient interpolation and kernel smoothing.

Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning. Meanwhile, a few recent works have provided theoretical justification and new insights on deep neural networks for estimating smooth functions in usual settings such…

Guarantees convergence for black-box variational inference without modifications.

VAE with noise model learns smoothed densities without seeing noisy data.

Bayesian inference for biochemical reaction networks using jump-diffusion approximations.

FLUID uses flows to unify filtering and smoothing for complex systems.

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference \emph{and learning} (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gauss…

Modelling exchangeable relational data can be described by \textit{graphon theory}. Most Bayesian methods for modelling exchangeable relational data can be attributed to this framework by exploiting different forms of graphons. However, the graphons adopted by existing Bayesian methods are either piecewise-constant fun…

New nonlinear smoothers improve state estimation in chaotic systems.

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…

Optimal inference in distributed quantile regression without stringent scaling conditions.

Scalable model checking for stochastic systems using Gaussian Processes and Bayesian Neural Networks.

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such time-varying smoothness is not accounted for, one can obtain misleading inf…

New method for causal inference with observed covariates improves learning rates.

Nonparametric density deconvolution and denoising using simulation-based inference

Proposes a method for inference in high-dimensional classification with non-differentiable surrogate losses.

Novel method for SDE calibration from sparse data using neural flows.

Unified framework for ensemble transport-based smoothing of non-Gaussian time series.

New method improves Kalman filtering and smoothing for large state spaces.

High-dimensional inference for sparse spectral precision matrices

We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon. Smoothing overcomes the non-smoothness inherent to the maximum margin structured prediction objective, and paves the way for the use of fast primal gradient-based optimization algorithms. We …

LogGENE uses log-cosh loss for deep learning in gene expression datasets, improving accuracy and interpretability.

Estimates convex hulls of smooth function images with error bounds.

Black-box variational inference tries to approximate a complex target distribution though a gradient-based optimization of the parameters of a simpler distribution. Provable convergence guarantees require structural properties of the objective. This paper shows that for location-scale family approximations, if the targ…

Improved model for non-smooth signals with complex spectra.

New methods for scoring function decomposition improve forecast evaluation.

New method improves generative model performance by fully conditioning variational posteriors.

New bounds show BBVI's gradient variance matches SGD conditions, improving parameterization efficiency.

Smoothing splines provide a powerful and flexible means for nonparametric estimation and inference. With a cubic time complexity, fitting smoothing spline models to large data is computationally prohibitive. In this paper, we use the theoretical optimal eigenspace to derive a low rank approximation of the smoothing spl…

ECI improves time series prediction uncertainty quantification by smoothing miscoverage error.

A debiasing method improves nonparametric regression's statistical properties.

The variational autoencoder (VAE) is a popular model for density estimation and representation learning. Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate. However, it is often overlooked that an overly-expressive inference model ca…

Label assignment problems with large state spaces are important tasks especially in computer vision. Often the pairwise interaction (or smoothness prior) between labels assigned at adjacent nodes (or pixels) can be described as a function of the label difference. Exact inference in such labeling tasks is still difficul…