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.
MixTS uses a mixture prior to analyze Thompson Sampling in multi-task learning.
Two EM algorithms estimate prior distributions in mixture of linear regressions.
End-to-end learnable Gaussian mixture priors improve diffusion models' exploration and expressiveness.
Plug-and-play L-GM-AMP improves CS recovery for any i.i.d. source prior.
In recent years, a rich variety of shrinkage priors have been proposed that have great promise in addressing massive regression problems. In general, these new priors can be expressed as scale mixtures of normals, but have more complex forms and better properties than traditional Cauchy and double exponential priors. W…
New pruning method retains model expressiveness for NLP tasks.
VampPrior Mixture Model improves clustering in DLVMs.
PDGMM-VAE uses adaptive priors for better ICA recovery.
We assume that a high-dimensional datum, like an image, is a compositional expression of a set of properties, with a complicated non-linear relationship between the datum and its properties. This paper proposes a factorial mixture prior for capturing latent properties, thereby adding structured compositionality to deep…
Proposes diffusion models using mixed Gaussian priors for better data representation.
Proposes scale mixture of NNGPs for more flexible stochastic processes.
In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions with a generalized inverse Gaussian density (EP-GIG). EP-GIG is a variant of generalized hyperbolic distributions, and the special cases inclu…
Humans perceive the seemingly chaotic world in a structured and compositional way with the prerequisite of being able to segregate conceptual entities from the complex visual scenes. The mechanism of grouping basic visual elements of scenes into conceptual entities is termed as perceptual grouping. In this work, we pro…
Study on Dirichlet process mixtures for clustering consistency.
HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.
Recent work has shown that deep generative models assign higher likelihood to out-of-distribution inputs than to training data. We show that a factor underlying this phenomenon is a mismatch between the nature of the prior distribution and that of the data distribution, a problem found in widely used deep generative mo…
Bayes factors and relative belief ratios are compared as measures of statistical evidence.
Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, beyond standard factorial "sparse" methodology. We derive a large …
Bayesian PROCOVA uses AI to adjust for covariates in RCTs.
We give tight concentration bounds for mixtures of martingales that are simultaneously uniform over (a) mixture distributions, in a PAC-Bayes sense; and (b) all finite times. These bounds are proved in terms of the martingale variance, extending classical Bernstein inequalities, and sharpening and simplifying prior wor…
Paper improves speech separation by using deep neural networks for more accurate density priors.
Patch priors have become an important component of image restoration. A powerful approach in this category of restoration algorithms is the popular Expected Patch Log-Likelihood (EPLL) algorithm. EPLL uses a Gaussian mixture model (GMM) prior learned on clean image patches as a way to regularize degraded patches. In th…
The study improves representation learning bounds using data-dependent Gaussian mixtures.
Proposes DSM priors for Bayesian neural networks to improve interpretability and robustness.
The paper studies multi-view representation learning with generalization guarantees and a new regularizer.
Paper introduces a new text clustering model using Beta-Liouville priors.
A novel Bayesian method for dynamic sparsity in Gaussian dynamic linear regression.
We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all -approximate algorithms are …
Many different methods to train deep generative models have been introduced in the past. In this paper, we propose to extend the variational auto-encoder (VAE) framework with a new type of prior which we call "Variational Mixture of Posteriors" prior, or VampPrior for short. The VampPrior consists of a mixture distribu…
One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for the da…
Extends Gaussian Process regression for handling multiple prior distributions.
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…
ARGUE combines expert networks for anomaly detection.
A new framework for robust policy learning in MDPs with linear mixture dynamics.
StrADiff separates sources from mixtures without labels, using structured priors.
Deep models memorize training data in geophysical inversion, leading to biased posterior distributions.
In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely us…
Difficult image segmentation problems, for instance left atrium MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Nonetheless, a single multivariate Gaussian is not an adequate model in cases with significant nonlinear shape variation or where the prior distri…
A new method uses mixture approximations to improve diffusion models for Bayesian inverse problems.
PIMA autoencoders discover shared features in multimodal scientific data.
Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs), where the joint distribution in a population follows a DAG at any single…
Paper presents a reparameterized DP-DLGMM for clustering.
Adaptive importance sampling for stochastic optimization is a promising approach that offers improved convergence through variance reduction. In this work, we propose a new framework for variance reduction that enables the use of mixtures over predefined sampling distributions, which can naturally encode prior knowledg…
In this paper, we propose a generalized scale mixture family of distributions, namely the Power Exponential Scale Mixture (PESM) family, to model the sparsity inducing priors currently in use for sparse signal recovery (SSR). We show that the successful and popular methods such as LASSO, Reweighted and Reweigh…
BN^2MF identifies unknown exposure patterns in environmental mixtures.
A new model improves clustering by reducing redundancy in mixture of local EPCAs.
The study assesses sensitivity to prior choices in Bayesian nonparametric models.
Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized comp…