Author name cluster

Kurt Cutajar

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5 papers

1 author row

ICLR Conference 2024 Conference Paper

Inherently Interpretable Time Series Classification via Multiple Instance Learning

Joseph Early
Gavin K. C. Cheung
Kurt Cutajar
Hanting Xie
Jas Kandola
Niall Twomey

Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multiple Instance Learning (MIL) to overcome this issue, and propose a new framework called MILLET: Multiple Instance Learning for Locally Explainable Time series classification. We apply MILLET to existing deep learning TSC models and show how they become inherently interpretable without compromising (and in some cases, even improving) predictive performance. We evaluate MILLET on 85 UCR TSC datasets and also present a novel synthetic dataset that is specially designed to facilitate interpretability evaluation. On these datasets, we show MILLET produces sparse explanations quickly that are of higher quality than other well-known interpretability methods. To the best of our knowledge, our work with MILLET is the first to develop general MIL methods for TSC and apply them to an extensive variety of domains.

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UAI Conference 2017 Conference Paper

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

Karl Krauth
Edwin V. Bonilla
Kurt Cutajar
Maurizio Filippone

and (iii) the use of well-targeted objective functions for the problem at hand. We investigate the capabilities and limitations of Gaussian process (GP) models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference; (ii) flexible kernels; and (iii) objective functions for hyperparameter learning alternative to the marginal likelihood. Our approach outperforms all previous GP methods on the MNIST dataset; performs comparatively to kernel-based methods using the RECTANGLES - IMAGE dataset; and breaks the 1% error-rate barrier in GP models on the MNIST 8 M dataset, while showing unprecedented scalability (8 million observations) in GP classification. Overall, our approach represents a significant breakthrough in kernel methods and GP models, bridging the gap between deep learning and kernel machines. In the kernel world, Gaussian process (GP; Rasmussen and Williams, 2006) models are attractive because they are elegant Bayesian nonparametric approaches to learning from data. Nevertheless, besides the limitations intrinsic to local kernel machines (Bengio et al. , 2005), it is clear that GP-based methods have not fully explored the desiderata mentioned above.

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UAI Conference 2017 Conference Paper

Bayesian Inference of Log Determinants

Jack K. Fitzsimons
Kurt Cutajar
Maurizio Filippone
Michael A. Osborne
Stephen J. Roberts

The log determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through to the training of Gaussian processes. Exact calculation of this term is often intractable when the size of the kernel matrix exceeds a few thousands. In the spirit of probabilistic numerics, we reinterpret the problem of computing the log determinant as a Bayesian inference problem. In particular, we combine prior knowledge in the form of bounds from matrix theory and evidence derived from stochastic trace estimation to obtain probabilistic estimates for the log determinant and its associated uncertainty within a given computational budget. Beyond its novelty and theoretic appeal, the performance of our proposal is competitive with state-of-the-art approaches to approximating the log determinant, while also quantifying the uncertainty due to budgetconstrained evidence.

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ICML Conference 2017 Conference Paper

Random Feature Expansions for Deep Gaussian Processes

Kurt Cutajar
Edwin V. Bonilla
Pietro Michiardi
Maurizio Filippone

The composition of multiple Gaussian Processes as a Deep Gaussian Process DGP enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbersome to construct. In this work we introduce a novel formulation of DGPs based on random feature expansions that we train using stochastic variational inference. This yields a practical learning framework which significantly advances the state-of-the-art in inference for DGPs, and enables accurate quantification of uncertainty. We extensively showcase the scalability and performance of our proposal on several datasets with up to 8 million observations, and various DGP architectures with up to 30 hidden layers.

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ICML Conference 2016 Conference Paper

Preconditioning Kernel Matrices

Kurt Cutajar
Michael A. Osborne
John P. Cunningham
Maurizio Filippone

The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to use the conjugate gradient algorithm, which relieves the constraints on both storage (the kernel matrix need not be stored) and computation (both stochastic gradients and parallelization can be used). Even so, conjugate gradient is not without its own issues: the conditioning of kernel matrices is often such that conjugate gradients will have poor convergence in practice. Preconditioning is a common approach to alleviating this issue. Here we propose preconditioned conjugate gradients for kernel machines, and develop a broad range of preconditioners particularly useful for kernel matrices. We describe a scalable approach to both solving kernel machines and learning their hyperparameters. We show this approach is exact in the limit of iterations and outperforms state-of-the-art approximations for a given computational budget.

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