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Found 9 papers, 4 papers with code
Learning relevant contextual variables within Bayesian Optimization
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions.
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into the optimization process, with the goal of converging towards the global optimum at a reduced cost.
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
We design a multi-task learning architecture for this task, with the goal of jointly eliciting the expert knowledge and minimizing the objective function.
Approximate Bayesian Computation with Domain Expert in the Loop
Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions.
Targeted Active Learning for Bayesian Decision-Making
Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way.
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization
Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data.
A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournaments
We propose a novel ranking model that combines the Bradley-Terry-Luce probability model with a nonnegative matrix factorization framework to model and uncover the presence of latent variables that influence the performance of top tennis players.
Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization
In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices.
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
We present novel understandings of the Gamma-Poisson (GaP) model, a probabilistic matrix factorization model for count data.
