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

Tue Dec 14 11:30 AM -- 12:15 PM (PST) @
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)

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