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.
UA-SABI uses surrogates to speed up Bayesian inference for expensive models.
New method uses low-fidelity simulations to efficiently infer parameters of high-fidelity models.
NoFAS combines variational inference and adaptive surrogate models for efficient inference of computationally expensive models.
Bayesian search optimizes exploration of feasible solutions under expensive constraints.
The complex and computationally expensive nature of landscape evolution models pose significant challenges in the inference and optimisation of unknown parameters. Bayesian inference provides a methodology for estimation and uncertainty quantification of unknown model parameters. In our previous work, we developed para…
We consider the class of optimization problems arising from computationally intensive L1-regularized M-estimators, where the function or gradient values are very expensive to compute. A particular instance of interest is the L1-regularized MLE for learning Conditional Random Fields (CRFs), which are a popular class of …
The graphics processing unit (GPU) has emerged as a powerful and cost effective processor for general performance computing. GPUs are capable of an order of magnitude more floating-point operations per second as compared to modern central processing units (CPUs), and thus provide a great deal of promise for computation…
This research simplifies Riemannian LBFGS for SPD manifolds.
Efficiently calibrates computationally expensive models using vine copulas.
Efficiently estimates SAGE values using causal structure learning.
In this paper, we focus on developing efficient sensitivity analysis methods for a computationally expensive objective function in the case that the minimization of it has just been performed. Here "computationally expensive" means that each of its evaluation takes significant amount of time, and therefore our m…
Proposes approximating computationally expensive explainability techniques using conformal regression.
Prevalidated ridge regression simplifies logistic regression for high-dimensional data.
The performance of policy gradient methods is sensitive to hyperparameter settings that must be tuned for any new application. Widely used grid search methods for tuning hyperparameters are sample inefficient and computationally expensive. More advanced methods like Population Based Training that learn optimal schedule…
Nonparametric models are versatile, albeit computationally expensive, tool for modeling mixture models. In this paper, we introduce spectral methods for the two most popular nonparametric models: the Indian Buffet Process (IBP) and the Hierarchical Dirichlet Process (HDP). We show that using spectral methods for the in…
Efficiently optimizes expensive functions with multi-step lookahead using one-shot optimization.
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) methods typically implement Bayesian inference which faces several challenges given a large number of parameters, complex and multimodal posterio…
Graph database outperforms in filtering ESG stocks efficiently.
New methods improve global optimisation for expensive functions using lookahead strategies.
Divide-and-conquer framework speeds up black-box inference for large data.
Approximate Bayesian computation (ABC) is now an established technique for statistical inference used in cases where the likelihood function is computationally expensive or not available. It relies on the use of a~model that is specified in the form of a~simulator, and approximates the likelihood at a~parameter value $…
In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks. The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial neural netwo…
Efficiently searches through Gaussian process kernels using symbolic representation and Bayesian optimization.
New SMC samplers improve stochastic optimisation efficiency.
The paper proposes a scalable framework for uncertainty quantification and propagation in surrogate-based Bayesian inference.
Markov random field (MRF) learning is intractable, and its approximation algorithms are computationally expensive. We target a small subset of MRF that is used frequently in computer vision. We characterize this subset with three concepts: Lattice, Homogeneity, and Inertia; and design a non-markov model as an alternati…
The problem of learning the structure of a high dimensional graphical model from data has received considerable attention in recent years. In many applications such as sensor networks and proteomics it is often expensive to obtain samples from all the variables involved simultaneously. For instance, this might involve …
In this work, sequence-to-sequence (seq2seq) models, originally developed for language translation, are used to predict the temporal evolution of complex, multi-physics computer simulations. The predictive performance of seq2seq models is compared to state transition models for datasets generated with multi-physics cod…
The estimation of unknown values of parameters (or hidden variables, control variables) that characterise a physical system often relies on the comparison of measured data with synthetic data produced by some numerical simulator of the system as the parameter values are varied. This process often encounters two major d…
Realistic evolutionary fitness landscapes are notoriously difficult to construct. A recent cutting-edge model of virus assembly consists of a dodecahedral capsid with corresponding packaging signals in three affinity bands. This whole genome/phenotype space consisting of genomes has been explored via comp…
A new method for efficient optimization of expensive simulations on HPC.
Quantum method speeds up VB estimation in machine learning.
Paper explores using bi-fidelity data to train neural networks for uncertainty quantification.
Recent studies have explored the use of deep generative models of speech spectra based of variational autoencoders (VAEs), combined with unsupervised noise models, to perform speech enhancement. These studies developed iterative algorithms involving either Gibbs sampling or gradient descent at each step, making them co…
Enhances SBI accuracy with multilevel Monte Carlo for expensive simulators.
It is typical for a machine learning system to have numerous hyperparameters that affect its learning rate and prediction quality. Finding a good combination of the hyperparameters is, however, a challenging job. This is mainly because evaluation of each combination is extremely expensive computationally; indeed, train…
A method for efficient CV estimates in Bayesian hierarchical models.
Normalizing flow regression approximates posterior distributions without additional sampling.
Efficient equivariant MobileNetV2 for medical applications on mobile devices.
The study analyzes and mitigates errors in PC-based causal discovery methods.
RandALO speeds up risk estimation for large datasets.
Engineers widely use Gaussian process regression framework to construct surrogate models aimed to replace computationally expensive physical models while exploring design space. Thanks to Gaussian process properties we can use both samples generated by a high fidelity function (an expensive and accurate representation …
New framework tackles high-dimensional reliability analysis using surrogate models and active subspaces.
New quadrature method using randomly pivoted Cholesky outperforms existing techniques.
New classifiers converge under large data, simplifying complex models.
Scientists and engineers rely on accurate mathematical models to quantify the objects of their studies, which are often high-dimensional. Unfortunately, high-dimensional models are inherently difficult, i.e. when observations are sparse or expensive to determine. One way to address this problem is to approximate the or…
Paper proposes using tree-based surrogate models for efficient Shapley computation.
Deconvolution microscopy has been extensively used to improve the resolution of the widefield fluorescent microscopy. Conventional approaches, which usually require the point spread function (PSF) measurement or blind estimation, are however computationally expensive. Recently, CNN based approaches have been explored a…