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6 papers · 1 filter
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
William J. Wilkinson, Simo Särkkä, Arno Solin
We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterior linearisation (PL) as extensions of Newton's method for optimising the paramet…
De-randomizing MCMC dynamics with the diffusion Stein operator
Zheyang Shen, Markus Heinonen, Samuel Kaski
Approximate Bayesian inference estimates descriptors of an intractable target distribution - in essence, an optimization problem within a family of distributions. For example, Lang…
Sparse Algorithms for Markovian Gaussian Processes
William J. Wilkinson, Arno Solin, Vincent Adam
Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series. Sparse Markovian Gaussian proces…
Deep State-Space Gaussian Processes
Zheng Zhao, Muhammad Emzir, Simo Särkkä
This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed Gaussian process (GP) prio…
Unbiased Loss Functions for Extreme Classification With Missing Labels
Erik Schultheis, Mohammadreza Qaraei, Priyanshu Gupta +1
The goal in extreme multi-label classification (XMC) is to tag an instance with a small subset of relevant labels from an extremely large set of possible labels. In addition to the…
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin, Peter Schulam, Eero Siivola +3
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based deci…