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[Submitted on 28 May 2024] View a PDF of the paper titled Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes, by Jihao Andreas Lin and Shreyas Padhy and Bruno Mlodozeniec and Javier Antor'an and Jos'e Miguel Hern'andez-Lobato View PDF Abstract:Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community. This paper focuses on iterative methods, which use linear system solvers, like conjugate gradients, alternating projections or stochastic gradient descent, to construct an estimate of the marginal likelihood gradient. We discuss three key improvements which are applicable across solvers: (i) a pathwise…

arXiv:2405.18437v1 Announce Type: new Abstract: Transductive inference has been widely investigated in few-shot image classification, but completely overlooked in the recent, fast growing literature on adapting vision-langage models like CLIP. This paper addresses the transductive zero-shot and few-shot CLIP classification challenge, in which inference is performed jointly across a mini-batch of unlabeled query samples, rather than treating each instance independently. We initially construct informative vision-text probability features, leading to a classification problem on the unit simplex set. Inspired by Expectation-Maximization (EM), our optimization-based classification objective models the data probability distribution for each class using a Dirichlet law. The minimization problem…