Search Results for author:
Found 7 papers, 3 papers with code
Aligned Multi-Task Gaussian Process
Building on the previous work by Kazlauskaiteet al. [2019], we include a separate monotonic warp of the input data to model temporal misalignment.
Bayesian nonparametric shared multi-sequence time series segmentation
In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics.
Compositional uncertainty in deep Gaussian processes
Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.
Modulating Surrogates for Bayesian Optimization
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.
Monotonic Gaussian Process Flow
We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations.
Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM).
Gaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner.
