Search Results for author:
Found 7 papers, 1 papers with code
What went wrong and when?\\ Instance-wise feature importance for time-series black-box models
Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature.
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).
Neural Networks with Cheap Differential Operators
Gradients of neural networks can be computed efficiently for any architecture, but some applications require computing differential operators with higher time complexity.
Scalable Gradients and Variational Inference for Stochastic Differential Equations
We derive reverse-mode (or adjoint) automatic differentiation for solutions of stochastic differential equations (SDEs), allowing time-efficient and constant-memory computation of pathwise gradients, a continuous-time analogue of the reparameterization trick.
Probing the Compositionality of Intuitive Functions
How do people learn about complex functional structure?
Testing MCMC code
Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively.
Active Learning of Model Evidence Using Bayesian Quadrature
Numerical integration is an key component of many problems in scientific computing, statistical modelling, and machine learning.
