I am still welcoming PhD applicants for 2022 at Imperial College London. We are a growing research group, with clear goals on what new abilities we want to develop in ML and neural networks. Topics: Invariances, neural arch search, (Bayesian) model selection, Gaussian processes.

Join us to discuss Conjugate Gradient based GP approximations! We make training easier by automatically setting approximation parameters like CG tolerance using marginal likelihood bounds. Today 5pm (London) / 9am PDT. Long talk and poster available at icml.cc/virtual/2021/poster/….

Current Conjugate Gradient Gaussian Processes require manual tuning to trade off accuracy and speed. Existing guidelines can give suboptimal results, without clear warnings. Our method tunes automatically, runs fewer CG steps, and performs better: arxiv.org/abs/2102.08314 👇1/6

I'm looking forward to speaking tomorrow. I will share some thoughts on: - How Gaussian processes can help deep learning - Recent work on accurate GP inference - What makes a method "exact", and to what extent recent methods live up to this Link below if you want to join!

Tomorrow 10 Dec at 11am GMT I will speak at the Bayesian Deep Learning Meetup about **Bayesian Model Selection** and how it can help architecture search. In a short 20 minutes we will discuss why we (Bayesians ∪ Deep Learners) should care, and approaches from now and the past.