Direct loss minimization algorithms for sparse Gaussian processes

Understand

The paper provides a thorough investigation of Direct loss minimization (DLM), which optimizes the posterior to minimize predictive loss, in sparse Gaussian processes.

• For the conjugate case, we consider DLM for log-loss and DLM for square loss showing a significant performance improvement in both cases.
• The application of DLM in non-conjugate cases is more complex because the logarithm of expectation in the log-loss DLM objective is often intractable and simple sampling leads to biased estimates of gradients.
• The paper makes two technical contributions to address this.