Study uses machine learning to recommend best solvers for slab transport problems.
problem Auto-selecting the best solvers for transport problems in uniform slabs.
method Three solvers (Richardson, diffusion synthetic acceleration, nonlinear diffusion acceleration) and five machine learning algorithms (linear discriminant analysis, K-nearest neighbors, support vector machine, random forest, neural networks) were tested.
result Random forest and K-nearest neighbors showed potential as best solvers for classification problems.
Method approximates covariance ellipsoid using random slabs or ellipsoids.
problem Approximating the covariance ellipsoid of a random vector.
method Constructing approximations using random slabs or ellipsoids generated from data.
result Approximations can be constructed with a sample size of N=c1dη−4log(2/η) or N=c1dη−2log(2/η) under minimal assumptions. Researchers prove existence of convex translators in slab regions in all dimensions.
problem Existence of translating solutions in slab regions.
method Proof in all dimensions n≥2; slab width πsecθ; convexity and regularity results for symmetrical translators. result Existence of convex translators in specific slab regions.
Develops generic spike-and-slab priors for high-dimensional linear regression.
problem Bayesian high-dimensional linear regression challenges.
method Proposes a class of generic spike-and-slab priors and a unified framework for theoretical assessment.
result Achieves nearly-optimal posterior contraction rate and model selection consistency under general conditions.
Spike-and-slab priors are improved for high-dimensional Bayesian regression.
problem Prohibitive computational costs for existing samplers in high-dimensional settings.
method Proposes Scalable Spike-and-Slab (S3) for high-dimensional Bayesian regression. result Improves computational cost to max{n2pt,np} per iteration, demonstrating significant speed-ups and quality gains. Paper proposes a new method for Bayesian linear regression using spike-and-slab priors.
problem Identifying predictors with similar relationships in linear regression models.
method Hierarchical Bayesian models with spike-and-slab priors and a Gibbs sampler.
result The proposed method outperforms previous methods in simulations and real data analysis.
Study minimal annuli in a slab, estimating their area.
problem Estimating the area of minimal annuli in a slab.
method Organized minimal annuli based on winding number, deduced convexity of length function, compared to catenoid waist area.
result Deduced convexity of length function and estimated area of minimal annuli.
In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive crossvalidatio…
In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive cross-validati…
2D simply connected translating solitons in slabs are convex and have entropy < 3.
problem Characterizing 2D translating solitons in slabs with entropy constraints.
method Analyzing the properties of translating solitons in slabs with entropy constraints.
result 2D simply connected translating solitons in slabs are convex and have entropy < 3.
The study restricts surfaces in a specific geometry to certain configurations, proving no annular ends can be contained in horizontal slabs.
problem Properly embedded surfaces with constant mean curvature in a specific geometric setting.
method Proof of geometric restrictions using slab and halfspace theorems.
result Surfaces with constant mean curvature are confined to specific configurations, including graphs over simply connected domains.
New method samples from piecewise smooth distributions using Hamiltonian Monte Carlo.
problem Sampling from distributions with discontinuous gradients.
method Generalized Randomized Hamiltonian Monte Carlo (GRHMC) for piecewise smooth targets.
result GRHMC processes sample from piecewise smooth target distributions with the desired distribution as the invariant distribution.
Consider a non-planar orientable minimal surface S in a slab which is possibly with genus or with more than two boundary components. We show that there exists a catenoidal waist W in the slab whose flux has the same vertical component as S such that Area(S)>= Area(W), provided the intersections of S with horizontal pla…
New algorithms improve Bayesian linear regression with spike-and-slab priors.
problem Efficiently sampling from Bayesian linear regression models with sparsity-inducing priors.
method Design of two sampling algorithms: Gibbs sampling and Stochastic Localization.
result Stochastic Localization sampler shows significant advantage for poorly designed data matrices.
We construct a compact, convex ancient solution of mean curvature flow in Rn+1 with O(1)×O(n) symmetry that lies in a slab of width π. We provide detailed asymptotics for this solution and show that, up to rigid motions, it is the only compact, convex, O(n)-invariant ancient solution that lies …
This paper speeds up OCSSVM training using SMO.
problem Training One-Class Slab SVMs is slow.
method Uses updated SMO to divide large problems into smaller, analytically solvable subproblems.
result Training OCSSVMs scales better with large datasets.
Finite entropy translating solitons in slabs have quantized entropy and unique structure.
problem Finite entropy translating solitons in slabs with finite genus and finite entropy.
method Analyzing wing numbers and using Morse theory for minimal surfaces.
result Entropy of these solitons is quantized into integer steps and unique structure proven.
The paper extends and applies a new shrinkage prior in Bayesian factor analysis.
problem Estimating the number of factors in sparse Bayesian factor analysis.
method Introduces and extends a generalized cumulative shrinkage process (CUSP) prior.
result Exchangeable spike-and-slab shrinkage priors imply increasing shrinkage as the column index increases.
We study stable immersed capillary hypersurfaces in a domain B which is either a half-space or a slab in the Euclidean space Rn+1. We prove that such a hypersurface Σ is rotationally symmetric in the following cases: (1) n=2, B is a slab and Σ has genus zero, (2) n≥2, $\mathc…
The X-ray transform on the periodic slab [0,1]×Tn, n≥0, has a non-trivial kernel due to the symmetry of the manifold and presence of trapped geodesics. For tensor fields gauge freedom increases the kernel further, and the X-ray transform is not solenoidally injective unless n=0. We characterize t…
We propose a flexible convex relaxation for the phase retrieval problem that operates in the natural domain of the signal. Therefore, we avoid the prohibitive computational cost associated with "lifting" and semidefinite programming (SDP) in methods such as PhaseLift and compete with recently developed non-convex techn…
New algorithms sample spike-and-slab priors efficiently in high dimensions.
problem Sampling from spike-and-slab priors in high-dimensional settings.
method Provably efficient algorithms for posterior sampling with sublinear measurement count.
result First provable algorithms for spike-and-slab posterior sampling without strong SNR assumptions.
A fast and scalable method for variable selection in high-dimensional Gaussian processes.
problem Inefficient variable selection in high-dimensional Gaussian processes.
method Developed a fast and scalable variational inference algorithm for spike and slab Gaussian processes.
result Consistently outperforms vanilla and sparse variational GPs while retaining similar runtimes.
Unlike R3, the homogeneous spaces E(−1,τ) have a great variety of entire vertical minimal graphs. In this paper we explore conditions which guarantees that a minimal surface in E(−1,τ) is such a graph. More specifically: we introduce the definition of a generalized slab in $\mathbb{E…
Study on stable CMC surfaces in slabs with boundary conditions.
problem Characterizing and proving properties of stable CMC surfaces in slabs.
method Analyzing stable constant mean curvature (CMC) hypersurfaces in product spaces with free boundary conditions.
result No stable CMC surface connects boundary components of a slab with width greater than a certain limit.
We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-the-art on texture synthesis and inpainting by parametric models. We also develop a novel RBM model with a spike-and-slab visible layer and bi…
In this work, we address the problem of solving a series of underdetermined linear inverse problems subject to a sparsity constraint. We generalize the spike-and-slab prior distribution to encode a priori correlation of the support of the solution in both space and time by imposing a transformed Gaussian process on the…
Ancient mean curvature flows confined to slabs, halfspaces, or full space.
problem Characterizing ancient solutions of mean curvature flow.
method Bi-halfspace theorem derived from a parabolic Omori-Yau maximum principle.
result Compact convex ancient mean curvature flows are confined to specific regions.
We study inference and learning based on a sparse coding model with `spike-and-slab' prior. As in standard sparse coding, the model used assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the applic…
Bayesian method selects sparse models efficiently with less bias.
problem Sparse model selection and regularization in Gaussian graphical models.
method Continuous spike-and-slab framework with EM algorithm for fast explorations.
result Efficient selection of sparse models with less bias compared to other methods.
We prove that the ends of a properly immersed simply or one connected minimal surface in H(2)xR contained in a slab of height less than πof H(2)xR, are multi-graphs. When such a surface is embedded then the ends are graphs. When embedded and simply connected, it is an entire graph.
Improved VB algorithm for high-dimensional logistic regression with theoretical guarantees.
problem Sparse high-dimensional logistic regression model selection.
method Spike and slab variational Bayes approximation.
result Optimal convergence rates in ℓ2 and prediction loss for sparse truths. The paper constructs ancient solutions to curvature flows in bounded and unbounded regions.
problem Understanding ancient solutions to curvature flows in bounded and unbounded regions.
method Constructing pancake-like and sausage-like ancient compact solutions.
result Ancient solutions to curvature flows in bounded and unbounded regions.
A new method discovers equations from data using Bayesian and kernel techniques.
problem Discovering equations from data is hard due to sparsity and noise.
method Kernel regression for function estimation and Bayesian spike-and-slab prior for uncertainty quantification.
result KBASS method outperforms state-of-the-art methods on benchmark tasks.
The paper confirms isoperimetric conjectures on cubes and Gaussian slabs.
problem Isoperimetric inequalities on slabs and cubes.
method Analysis of weighted Riemannian manifolds and product spaces.
result The isoperimetric conjecture on the three-dimensional cube is confirmed for a new range of relative volumes.
Bayesian SPCA method tackles orthogonality constraint with spike and slab prior.
problem Bayesian SPCA method for high-dimensional data with orthogonality constraint.
method Parameter-expanded coordinate ascent variational inference (PX-CAVI) with spike and slab prior.
result PX-CAVI algorithm outperforms existing SPCA approaches in performance.
We are interested in solving the multiple measurement vector (MMV) problem for instances, where the underlying sparsity pattern exhibit spatio-temporal structure motivated by the electroencephalogram (EEG) source localization problem. We propose a probabilistic model that takes this structure into account by generalizi…
Bayesian l0-regularized least squares is a variable selection technique for high dimensional predictors. The challenge is optimizing a non-convex objective function via search over model space consisting of all possible predictor combinations. Spike-and-slab (a.k.a. Bernoulli-Gaussian) priors are the gold standard f…
The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction. One design choice for the model is the number of latent variables. We present a spike and slab prior for the GP-LVM and propose an efficient variational inference procedure that gives a lower …
The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.
problem Sparse high-dimensional linear regression with random design and unknown error variance.
method Analysis of posterior concentration rates, employing techniques to address model misspecification.
result Adaptiveness and concentration rates of the posterior for sparse high-dimensional linear regression.
In this note we present a generative model of natural images consisting of a deep hierarchy of layers of latent random variables, each of which follows a new type of distribution that we call rectified Gaussian. These rectified Gaussian units allow spike-and-slab type sparsity, while retaining the differentiability nec…
The study proves optimal isoperimetric regions in manifolds with density.
problem Finding optimal regions with minimal boundary area in manifolds with density.
method Proving existence of isoperimetric regions and using subgroup actions.
result Isoperimetric regions in product manifolds are slabs.
Bayesian neural network achieves nearly optimal performance in Besov space.
problem Bayesian neural networks in Besov space.
method Spike-and-slab prior and shrinkage prior for posterior convergence rate.
result The posterior convergence rate is nearly minimax and adaptive to unknown smoothness.
In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, a…
We consider factoring low-rank tensors in the presence of outlying slabs. This problem is important in practice, because data collected in many real-world applications, such as speech, fluorescence, and some social network data, fit this paradigm. Prior work tackles this problem by iteratively selecting a fixed number …
We consider the problem of object recognition with a large number of classes. In order to overcome the low amount of labeled examples available in this setting, we introduce a new feature learning and extraction procedure based on a factor model we call spike-and-slab sparse coding (S3C). Prior work on S3C has not prio…
We consider the problem of using a factor model we call {\em spike-and-slab sparse coding} (S3C) to learn features for a classification task. The S3C model resembles both the spike-and-slab RBM and sparse coding. Since exact inference in this model is intractable, we derive a structured variational inference procedure …
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularis…