A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Unified approach improves accuracy in private estimation.
Graph neural networks extend neural Bayes estimators to irregular spatial data.
This paper compares classical parametric methods with recently developed Bayesian methods for system identification. A Full Bayes solution is considered together with one of the standard approximations based on the Empirical Bayes paradigm. Results regarding point estimators for the impulse response as well as for conf…
Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is that it underestimates the uncertainty of model variables (sometimes severely) and provides no information about model variable cova…
Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing a probabilistic framework based on the theory of random labeled point…
Mean Field Variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is its (sometimes severe) underestimates of the uncertainty of model variables and lack of information about model variable covariance.…
Neural point estimators improve parameter estimation from replicated data.
When related learning tasks are naturally arranged in a hierarchy, an appealing approach for coping with scarcity of instances is that of transfer learning using a hierarchical Bayes framework. As fully Bayesian computations can be difficult and computationally demanding, it is often desirable to use posterior point es…
New estimators outperform maximum likelihood without hyper-parameter estimation.
The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation. We provide general conditions for obtaining optimal risk bounds for point estimates acquired from mean-field variational Bayesian inference. The conditions pertain to the existence of cer…
Deep learning improves Bayes factor computation for likelihood-free models.
The sigma-point filters, such as the UKF, which exploit numerical quadrature to obtain an additional order of accuracy in the moment transformation step, are popular alternatives to the ubiquitous EKF. The classical quadrature rules used in the sigma-point filters are motivated via polynomial approximation of the integ…
Neural Empirical Bayes estimates source distributions from noisy simulations.
Bayesian method with Gaussian process priors achieves optimal convergence rates for regression function and its derivatives.
Empirical Bayes rates via variational approximations and prior decomposition.
Naive Bayes estimator is widely used in text classification problems. However, it doesn't perform well with small-size training dataset. We propose a new method based on Naive Bayes estimator to solve this problem. A correlation factor is introduced to incorporate the correlation among different classes. Experimental r…
Researchers estimate optimal PAC-Bayes bounds using Hamiltonian Monte Carlo.
A new method estimates Bayes error for deep networks, suggesting they may have reached the limit.
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks. The method is based on linear discriminant analysis and provides Bayes optimal single point estimates for the weight values.
New method for high-dimensional linear regression using empirical Bayes.
New method reduces computational cost for estimating PAC-Bayes bounds.
We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for estimating Gaussian location mixture densities in -dimensions from independent observations. Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization. We prove finite sample results on the Hellinger accurac…
PAS improves estimation of multiple means using ML predictions and shrinkage.
A new sequential method estimates Poisson means in streaming data, achieving optimality and efficiency.
We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier. Our learning to benchmark framework improves on previ…
Neural Bayes methods simplify fitting complex bivariate extremal models.
The paper analyzes high-dimensional linear regression using parametric empirical Bayes methods.
New algorithm for signal estimation in noisy matrix models.
A new framework for clustering with uncertainty quantification.
EB-PCA reduces noise in high-dimensional PCA by estimating a joint prior distribution.
Efficient neural Bayes estimators for censored peaks-over-threshold models improve inference speed and accuracy.
New method handles unknown task boundaries in continual learning.
In data science and machine learning, hierarchical parametric models, such as mixture models, are often used. They contain two kinds of variables: observable variables, which represent the parts of the data that can be directly measured, and latent variables, which represent the underlying processes that generate the d…
Paper estimates FPR of Bayes classifier using soft labels.
Study on the structure of classifier boundaries in DNA sequencing.
We consider parallel global optimization of derivative-free expensive-to-evaluate functions, and propose an efficient method based on stochastic approximation for implementing a conceptual Bayesian optimization algorithm proposed by Ginsbourger et al. (2007). At the heart of this algorithm is maximizing the information…
We propose an empirical Bayes estimator based on Dirichlet process mixture model for estimating the sparse normalized mean difference, which could be directly applied to the high dimensional linear classification. In theory, we build a bridge to connect the estimation error of the mean difference and the misclassificat…
Paper extends Bayes Theorem for interval probability estimates.
We develop a new framework of uncertainty variables to model uncertainty. An uncertainty variable is characterized by an uncertainty set, in which its realization is bound to lie, while the conditional uncertainty is characterized by a set map, from a given realization of a variable to a set of possible realizations of…
Improved model-based estimation through tempered Bayes filter.
We develop a class of rules spanning the range between quadratic discriminant analysis and naive Bayes, through a path of sparse graphical models. A group lasso penalty is used to introduce shrinkage and encourage a similar pattern of sparsity across precision matrices. It gives sparse estimates of interactions and pro…
Unified empirical and variational Bayes for unnormalized densities.
New bound improves on weighted majority vote risk estimation.
Bayes posterior yields worse predictions than simpler methods in deep neural networks.
In this study, we consider an empirical Bayes method for Boltzmann machines and propose an algorithm for it. The empirical Bayes method allows estimation of the values of the hyperparameters of the Boltzmann machine by maximizing a specific likelihood function referred to as the empirical Bayes likelihood function in t…
PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our …
Deep neural networks are optimal for dependent data using PAC-Bayes bounds.
New PAC-Bayes meta-learning method improves few-shot learning accuracy and calibration.