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Found 10 papers, 4 papers with code
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
A systematic numerical study reveals that parallel implementations of SMC and MCMC are comparable to serial implementations in terms of performance and total cost, and they achieve accuracy at or beyond the state-of-the-art (SOTA) methods like deep ensembles at convergence, along with substantially improved uncertainty quantification (UQ)--in particular, epistemic UQ.
SMC Is All You Need: Parallel Strong Scaling
The Bayesian posterior distribution can only be evaluated up-to a constant of proportionality, which makes simulation and consistent estimation challenging.
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.
Bayesian Deep Learning with Multilevel Trace-class Neural Networks
In particular, an MLMC method that was introduced is used to approximate posterior expectations of Bayesian TNN models with optimal computational complexity, and this is mathematically proved.
Sparse online variational Bayesian regression
Furthermore, for p unknown covariates the method can be implemented exactly online with a cost of $O(p^3)$ in computation and $O(p^2)$ in memory per iteration -- in other words, the cost per iteration is independent of n, and in principle infinite data can be considered.
On Unbiased Estimation for Discretized Models
In this article, we consider computing expectations w. r. t.
Bayesian Inference
Computation
Numerical Analysis
Numerical Analysis
Methodology
Materials Fingerprinting Classification
Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates.
Cluster, Classify, Regress: A General Method For Learning Discountinous Functions
This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous.
Unbiased inference for discretely observed hidden Markov model diffusions
The resulting estimator leads to inference without a bias from the time-discretisation as the number of Markov chain iterations increases.
Bayesian Inference
Methodology
Probability
Computation
65C05 (primary), 60H35, 65C35, 65C40 (secondary)
