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• W3097877554 abstract "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. First, a new method using product sampling is proposed, which gives unbiased estimates of gradients (uPS) for the objective function. Second, a theoretical analysis of biased Monte Carlo estimates (bMC) shows that stochastic gradient descent converges despite the biased gradients. Experiments demonstrate empirical success of DLM. A comparison of the sampling methods shows that, while uPS is potentially more sample-efficient, bMC provides a better tradeoff in terms of convergence time and computational efficiency." @default.
• W3097877554 created "2020-11-09" @default.
• W3097877554 creator A5004165329 @default.
• W3097877554 creator A5013618541 @default.
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• W3097877554 date "2020-04-07" @default.
• W3097877554 modified "2023-09-27" @default.
• W3097877554 title "Direct loss minimization algorithms for sparse Gaussian processes" @default.
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• W3097877554 hasPublicationYear "2020" @default.
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