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Found 15 papers, 2 papers with code
Batch and match: black-box variational inference with a score-based divergence
We analyze the convergence of BaM when the target distribution is Gaussian, and we prove that in the limit of infinite batch size the variational parameter updates converge exponentially quickly to the target mean and covariance.
Kernel Density Bayesian Inverse Reinforcement Learning
Inverse reinforcement learning~(IRL) is a powerful framework to infer an agent's reward function by observing its behavior, but IRL algorithms that learn point estimates of the reward function can be misleading because there may be several functions that describe an agent's behavior equally well.
Multi-fidelity Monte Carlo: a pseudo-marginal approach
A key challenge in applying MCMC to scientific domains is computation: the target density of interest is often a function of expensive computations, such as a high-fidelity physical simulation, an intractable integral, or a slowly-converging iterative algorithm.
Slice Sampling Reparameterization Gradients
Slice sampling is a Markov chain Monte Carlo algorithm for simulating samples from probability distributions; it only requires a density function that can be evaluated point-wise up to a normalization constant, making it applicable to a variety of inference problems and unnormalized models.
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation
Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior.
Active multi-fidelity Bayesian online changepoint detection
We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints.
Power posteriors do not reliably learn the number of components in a finite mixture
Increasingly, though, data science papers suggest potential alternatives beyond vanilla FMMs, such as power posteriors, coarsening, and related methods.
Finite mixture models do not reliably learn the number of components
In this paper, we add rigor to data-analysis folk wisdom by proving that under even the slightest model misspecification, the FMM component-count posterior diverges: the posterior probability of any particular finite number of components converges to 0 in the limit of infinite data.
Weighted Meta-Learning
Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples.
A Bayesian Nonparametric View on Count-Min Sketch
The count-min sketch is a time- and memory-efficient randomized data structure that provides a point estimate of the number of times an item has appeared in a data stream.
Edge-exchangeable graphs and sparsity (NIPS 2016)
Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices.
Edge-exchangeable graphs and sparsity
We show that, unlike node exchangeability, edge exchangeability encompasses models that are known to provide a projective sequence of random graphs that circumvent the Aldous-Hoover Theorem and exhibit sparsity, i. e., sub-quadratic growth of the number of edges with the number of nodes.
Completely random measures for modeling power laws in sparse graphs
Since individual network datasets continue to grow in size, it is necessary to develop models that accurately represent the real-life scaling properties of networks.
Priors on exchangeable directed graphs
Directed graphs occur throughout statistical modeling of networks, and exchangeability is a natural assumption when the ordering of vertices does not matter.
An iterative step-function estimator for graphons
Exchangeable graphs arise via a sampling procedure from measurable functions known as graphons.
