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Found 12 papers, 8 papers with code
Towards a Framework for a New Research Ecosystem
A major gap exists between the conceptual suggestion of how much a nation should invest in science, innovation, and technology, and the practical implementation of what is done.
Learning variational autoencoders via MCMC speed measures
Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximizing an Evidence Lower Bound (ELBO).
Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data
Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods.
How Good are Low-Rank Approximations in Gaussian Process Regression?
In particular, we bound the Kullback-Leibler divergence between an exact GP and one resulting from one of the afore-described low-rank approximations to its kernel, as well as between their corresponding predictive densities, and we also bound the error between predictive mean vectors and between predictive covariance matrices computed using the exact versus using the approximate GP.
Entropy-based adaptive Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution.
Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences
We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to derive a general and scalable inferential framework for marked point processes.
How Good are Low-Rank Approximations in Gaussian Process Regression?
In particular, we bound the Kullback-Leibler divergence between an exact GP and one resulting from one of the afore-described low-rank approximations to its kernel, as well as between their corresponding predictive densities, and we also bound the error between predictive mean vectors and between predictive covariance matrices computed using the exact versus using the approximate GP.
Gradient-based Adaptive Markov Chain Monte Carlo
We introduce a gradient-based learning method to automatically adapt Markov chain Monte Carlo (MCMC) proposal distributions to intractable targets.
Copula-like Variational Inference
This family is based on new copula-like densities on the hypercube with non-uniform marginals which can be sampled efficiently, i. e. with a complexity linear in the dimension of state space.
Bayesian prediction of jumps in large panels of time series data
We take a new look at the problem of disentangling the volatility and jumps processes of daily stock returns.
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data.
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
We present a scalable approach to performing approximate fully Bayesian inference in generic state space models.
