Timezone: »

 
Sparse Gaussian Processes for Stochastic Differential Equations
Prakhar Verma · Vincent ADAM · Arno Solin

Tue Dec 14 11:30 AM -- 12:15 PM (PST) @
in The Symbiosis of Deep Learning and Differential Equations »
Event URL: https://openreview.net/forum?id=yk3ghzrAVnp »

We frame the problem of learning stochastic differential equations (SDEs) from noisy observations as an inference problem and aim to maximize the marginal likelihood of the observations in a joint model of the latent paths and the noisy observations. As this problem is intractable, we derive an approximate (variational) inference algorithm and propose a novel parameterization of the approximate distribution over paths using a sparse Markovian Gaussian process. The approximation is efficient in storage and computation, allowing the usage of well-established optimizing algorithms such as natural gradient descent. We demonstrate the capability of the proposed method on the Ornstein-Uhlenbeck process.

Author Information

Prakhar Verma (Aalto University)
Vincent ADAM (UCL)

PhD in computational neuroscience and machine learning at the Gatsby Unit

Arno Solin (Aalto University)

More from the Same Authors