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.
Review of priors in Bayesian deep learning models.
We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to be inferred, and its hyper-parameters are determined by cross validation. Then we formulate network inference as a matrix completion problem…
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploi…
A parametrization of hypergraphs based on the geometry of points in is developed. Informative prior distributions on hypergraphs are induced through this parametrization by priors on point configurations via spatial processes. This prior specification is used to infer conditional independence models or M…
Proposes diffusion models using mixed Gaussian priors for better data representation.
Bayesian priors improve neural network performance on weak signals.
PDGMM-VAE uses adaptive priors for better ICA recovery.
This work considers an estimation task in compressive sensing, where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre-measurement noise (interference, or clutter) as well as post-measurement noise, in the specific setting where some (perhaps limited) prior knowl…
Proposes I-prior extension for additive interaction models.
Regularization methods, specifically those which directly alter weights like and , are an integral part of many learning algorithms. Both the regularizers mentioned above are formulated by assuming certain priors in the parameter space and these assumptions, in some cases, induce sparsity in the parameter sp…
HS-BQR extends horseshoe prior for Bayesian quantile regression.
In (exploratory) factor analysis, the loading matrix is identified only up to orthogonal rotation. For identifiability, one thus often takes the loading matrix to be lower triangular with positive diagonal entries. In Bayesian inference, a standard practice is then to specify a prior under which the loadings are indepe…
New meta-reinforcement learning method improves performance in finite-horizon MDPs.
Thompson sampling used for linear bandits with normal-gamma priors.
Spike-and-slab priors are popular Bayesian solutions for high-dimensional linear regression problems. Previous theoretical studies on spike-and-slab methods focus on specific prior formulations and use prior-dependent conditions and analyses, and thus can not be generalized directly. In this paper, we propose a class o…
This study explores how choosing noninformative priors affects Thompson Sampling in multiparameter bandit models.
MAXENT method outperforms ML in sparse data with specific prior correlations.
We propose a probabilistic framework to directly insert prior knowledge in reinforcement learning (RL) algorithms by defining the behaviour policy as a Bayesian posterior distribution. Such a posterior combines task specific information with prior knowledge, thus allowing to achieve transfer learning across tasks. The …
New method uses trainable activations to make BNNs behave like GPs.
Bayesian framework optimizes 3D view selection for specific tasks.
Proposes a method to integrate prior knowledge into trajectory prediction models.
Bayesian neural networks with Mercer priors for interpretable uncertainty quantification.
We present a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables. Our method accommodates not just the standard signs, but provides for context-specific signs as well. We show how the various signs translate into order constraints on the network …
Learning the network structure underlying data is an important problem in machine learning. This paper introduces a novel prior to study the inference of scale-free networks, which are widely used to model social and biological networks. The prior not only favors a desirable global node degree distribution, but also ta…
Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many important datasets, suc…
CONCERT improves transfer learning by borrowing partial information from auxiliary datasets.
Paper introduces a method to generate physically feasible dynamics with physical priors.
New findings suggest latent regularization is unnecessary for high-quality image generation.
Low-rank matrix estimation from incomplete measurements recently received increased attention due to the emergence of several challenging applications, such as recommender systems; see in particular the famous Netflix challenge. While the behaviour of algorithms based on nuclear norm minimization is now well understood…
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting. Our approach is based on flexible Gaussian process priors for the generative mappings and interchangeable Dirichlet process priors to learn the structure. The introduction of…
Improves transparency and incorporates prior knowledge in Gaussian Process models.
Study explores geometric structure and prior for beta-logistic distribution.
A VB method for high-dimensional regression with student-t priors achieves nearly optimal performance and computational efficiency.
This paper improves Gaussian process predictions by integrating prior knowledge.
Study shows priors are crucial for accurate causal learning from unlabeled data.
Unified framework for data-driven priors in Bayesian inverse problems
Optimality of TS with noninformative priors proven for Pareto model.
Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafted image priors such as sparsity and low-rank. Unlike learned generative priors they do not require any training over large datasets. However…
Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to …
Additive Bayesian networks are types of graphical models that extend the usual Bayesian generalized linear model to multiple dependent variables through the factorisation of the joint probability distribution of the underlying variables. When fitting an ABN model, the choice of the prior of the parameters is of crucial…
While Bayesian neural networks have many appealing characteristics, current priors do not easily allow users to specify basic properties such as expected lengthscale or amplitude variance. In this work, we introduce Poisson Process Radial Basis Function Networks, a novel prior that is able to encode amplitude stationar…
Novel unsupervised audio source separation using generative priors.
High-dimensional shrinkage risk depends on the default prior for the common scale.
Bayesian deep learning uses function-space priors to improve model uncertainty and robustness.
Bayesian method uses deep learning prior for CT reconstruction.
Bayesian neural networks incorporate domain knowledge through variational inference.
A new framework improves tensor completion accuracy by considering numerical priors.
Deep convolutional neural networks are known to specialize in distilling compact and robust prior from a large amount of data. We are interested in applying deep networks in the absence of training dataset. In this paper, we introduce deep audio prior (DAP) which leverages the structure of a network and the temporal in…