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.
Bayesian Markowitz portfolio problem shows entropy regularization is ineffective.
New approach to portfolio optimization shows entropy regularization is ineffective.
Generalization is essential for deep learning. In contrast to previous works claiming that Deep Neural Networks (DNNs) have an implicit regularization implemented by the stochastic gradient descent, we demonstrate explicitly Bayesian regularizations in a specific category of DNNs, i.e., Convolutional Neural Networks (C…
Bayesian framework for image inversion using regularization by denoising.
Bayesian priors and penalties are equivalent in variational inference.
We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback. Using PAC-Bayesian analysis, we derive a new generalization bound for the truncated inverse propensity score estimator. We apply the bound to a class of Bayesian policies, which motivates a novel, pote…
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whos…
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equivalence provides a new understanding of kernel Bayesian inference. Moreover, the optimization problem induces a new regularization for the …
Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.
Unified framework for analyzing pessimism in off-policy learning with regularized importance sampling.
Bayesian regularization improves policy performance in noisy MDPs.
Regularization improves generalization in Bayesian RL, shown through algorithmic stability.
This paper introduces NPR, a technique to improve Bayesian inference for multi-modal, high-dimensional simulations.
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learni…
Improves Bayesian neural networks inference efficiency and accuracy.
Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors can affect posterior distributions through Bayes' rule, imposing posterior regularization is arguably more direct and in s…
Sparse Bayesian Optimization (SEBO) finds interpretable configurations.
Bayesian -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 paper connects three machine learning methods to reduce generalization errors.
Novel regularization for Vision Transformers improves model generalization and sparsity.
Entropy regularization improves interpretability of probabilistic clustering models.
This paper improves Bayesian inference for predictive models with limited data.
A widely applicable Bayesian information criterion (Watanabe, 2013) is applicable for both regular and singular models in the model selection problem. This criterion tends to overestimate the log marginal likelihood. We identify an overestimating term of a widely applicable Bayesian information criterion. Adjustment of…
Bayesian networks are now being used in enormous fields, for example, diagnosis of a system, data mining, clustering and so on. In spite of their wide range of applications, the statistical properties have not yet been clarified, because the models are nonidentifiable and non-regular. In a Bayesian network, the set of …
This research improves binary classification by balancing overfitting and generalization with a novel Bayesian approach.
A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regularization often shrinks the estimator for relevant features, which leads to incorrect feature selection. Motivated by the above-mentioned issue…
Unified analysis for nonlinear parametric models in Bayesian optimization.
Adaptive sparseness enhances robust regression using MCC and ARD.
Bayesian approach learns linear networks from high-dimensional data.
We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities. Bayesian neural networks with Gaussian priors are well known to induce an L2, "weight decay", regularization. Our results characterize a more intricate regularization effect at the level of the unit activat…
Bayesian method for estimating functional graphical models from neuroimaging data.
Deep neural networks (DNNs) often require good regularizers to generalize well. Currently, state-of-the-art DNN regularization techniques consist in randomly dropping units and/or connections on each iteration of the training algorithm. Dropout and DropConnect are characteristic examples of such regularizers, that are …
RegVar quantifies uncertainty in deep learning networks by measuring sensitivity to regularization.
Regularization and Bayesian methods for system identification have been repopularized in the recent years, and proved to be competitive w.r.t. classical parametric approaches. In this paper we shall make an attempt to illustrate how the use of regularization in system identification has evolved over the years, starting…
Bayesian regularization tackles collinearity in large-scale systems with correlated inputs.
Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics, and consumer analysis. However, its basic property as a learning machine is not yet clarified, as it is not a regular statistical model, resulting that theoretical optimization me…
The paper analyzes methods for sparse Bayesian regression in nonlinear system identification.
Bayesian methods detect clusters in noisy data more reliably.
Study evaluates various regularization methods for electricity price forecasting.
SymCircuit learns PC structure via entropy-regularized RL, improving inference efficiency and accuracy.
In this paper, we propose a new method to overcome catastrophic forgetting by adding generative regularization to Bayesian inference framework. Bayesian method provides a general framework for continual learning. We could further construct a generative regularization term for all given classification models by leveragi…
Autoencoders and their variations provide unsupervised models for learning low-dimensional representations for downstream tasks. Without proper regularization, autoencoder models are susceptible to the overfitting problem and the so-called posterior collapse phenomenon. In this paper, we introduce a quantization-based …
Bayesian deep learning faces posterior collapse due to likelihood vs. prior competition.
Bayesian framework for encoding uncertainty and inducing sparsity.
Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the r…
Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of wei…
Variational Laplace improves Bayesian neural network performance without sampling.
New sampling method using regularized Wasserstein proximal for Gibbs distributions.