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arXiv research

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.

168,657 papers · 148 categories

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10 results for “Linearized-Laplace”

Reparameterization invariance in approximate Bayesian inferencecs.LG

The paper addresses the invariance issue in Bayesian neural networks using linearized Laplace approximation.

problem Bayesian neural networks fail to maintain invariance under reparameterization, leading to different posterior densities for identical functions.

method Developed a geometric view of reparameterizations and a Riemannian diffusion process to extend reparameterization invariance to neural network predictive.

result Empirically improved posterior fit through approximate posterior sampling.

Bayesian neural networks reparameterization invariance linearized Laplace approximation Riemannian diffusion posterior sampling

2024-06-05abs ↗pdf ↗

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimizationcs.LG

LLA shows strong performance in Bayesian optimization but has unbounded search space issues.

problem Applying LLA in unbounded search spaces for Bayesian optimization.

method Linearized-Laplace approximation applied to Bayesian optimization problems.

result LLA demonstrates strong performance but also presents unbounded search space challenges.

Bayesian optimization Linearized-Laplace Gaussian process unbounded search space

2023-04-17abs ↗pdf ↗

Bayesian Deep Learning via Subnetwork Inferencecs.LG

Bayesian deep learning method using subnetwork inference.

problem Improving deep neural networks' calibration and efficiency.

method Perform inference over a subset of model weights, keeping others as point estimates.

result Subnetwork inference enables accurate predictive posteriors without full network approximations.

Bayesian deep learning subnetwork inference Laplace approximation predictive uncertainty

2020-10-28abs ↗pdf ↗

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernelsstat.ML

New method optimizes hyperparameters in deep learning models efficiently.

problem Manual hyperparameter tuning in deep learning models is inefficient and requires expertise.

method Introduces lower bounds to the linearized Laplace approximation of the marginal likelihood using neural tangent kernels.

result Optimization of hyperparameters can be significantly accelerated using the method.

hyperparameter optimization deep learning Bayesian model selection stochastic gradients neural tangent kernel

2023-06-06abs ↗pdf ↗

Improving the Linearized Laplace Approximation via Quadratic Approximationsstat.ML

QLA improves Bayesian uncertainty estimation for DNNs without increasing computational cost.

problem Overconfident out-of-distribution predictions from DNNs.

method Proposes Quadratic Laplace Approximation (QLA) to improve Bayesian uncertainty quantification.

result QLA yields modest yet consistent uncertainty estimation improvements over Linearized Laplace Approximation (LLA) on five regression datasets.

Bayesian uncertainty Deep neural networks Quadratic approximation Laplace approximation Prediction

2026-02-03abs ↗pdf ↗

Variational Linearized Laplace Approximation for Bayesian Deep Learningstat.ML

A new method for efficient uncertainty estimation in deep learning models.

problem High computational costs in uncertainty estimation for deep neural networks.

method Variational sparse Gaussian Process approximation of the Linearized Laplace Approximation.

result Sub-linear training time and improved performance compared to existing methods.

Bayesian deep learning uncertainty estimation variational methods Gaussian processes deep neural networks

2023-02-24abs ↗pdf ↗

Semicontinuity of eigenvalues under intrinsic flat convergencemath.DG

We use the theory of rectifiable metric spaces to define a Dirichlet energy of Lipschitz functions defined on the support of integral currents. This energy is obtained by integration of the square of the norm of the tangential derivative, or equivalently of the approximate local dilatation, of the Lipschitz functions. …

2014-01-20abs ↗pdf ↗

Laplace Approximation for Bayesian Tensor Network Kernel Machinesstat.ML

A novel Laplace-approximated Bayesian Tensor Network Kernel Machine (LA-TNKM) provides principled uncertainty estimates.

problem How to provide principled uncertainty estimates for tensor network kernel machines.

method Employing a linearized Laplace approximation for Bayesian inference.

result Consistently matches or surpasses Gaussian Processes and BNNs across diverse UCI regression benchmarks.

Bayesian Tensor Network Kernel Machine Laplace Approximation Uncertainty Estimation Tensor Networks Kernel Machines

2026-04-29abs ↗pdf ↗

Linearization Turns Neural Operators into Function-Valued Gaussian Processescs.LG

LUNO linearizes neural operators to quantify their predictive uncertainty.

problem Quantifying the predictive error of neural operators for high-stakes simulations.

method Model linearization to push weight-space uncertainty forward to predictions.

result LUNO provides a practical and theoretically sound way to apply Bayesian methods to neural operators.

neural operators Bayesian uncertainty partial differential equations function-valued Gaussian process model linearization

2024-06-07abs ↗pdf ↗

A Study of Bayesian Neural Network Surrogates for Bayesian Optimizationcs.LG

Bayesian optimization uses BNNs as efficient surrogate models for expensive function evaluations.

problem Optimizing expensive objective functions using Gaussian process surrogates.

method Study of Bayesian neural networks (BNNs) as alternatives to standard Gaussian process (GP) surrogates for optimization.

result Infinite-width BNNs are particularly promising, especially in high dimensions.

Bayesian optimization Bayesian neural networks Gaussian processes Hamiltonian Monte Carlo Deep ensembles

2023-05-31abs ↗pdf ↗