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Found 11 papers, 1 papers with code
Learning Insulin-Glucose Dynamics in the Wild
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics.
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Reproducibility, that is obtaining similar results as presented in a paper or talk, using the same code and data (when available), is a necessary step to verify the reliability of research findings.
Interpretable VAEs for nonlinear group factor analysis
Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data.
Neural Granger Causality
We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data.
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series
Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed.
Expectation-Maximization for Learning Determinantal Point Processes
However, log-likelihood is non-convex in the entries of the kernel matrix, and this learning problem is conjectured to be NP-hard.
Approximate Inference in Continuous Determinantal Processes
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion.
Multiresolution Gaussian Processes
We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes.
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data
Applications of Bayesian nonparametric methods require learning and inference algorithms which efficiently explore models of unbounded complexity.
Sharing Features among Dynamical Systems with Beta Processes
We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors.
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.
