74 research outputs found
Probabilistic multiple kernel learning
The integration of multiple and possibly heterogeneous information sources for an overall decision-making process has been an open and unresolved research direction in computing science since its very beginning. This thesis attempts to address parts of that direction by proposing probabilistic data integration algorithms for multiclass decisions where an observation of interest is assigned to one of many categories based on a plurality of information channels
Spatio-temporal Bayesian on-line changepoint detection with model selection
Bayesian On-line Changepoint Detection is extended to on-line model selection
and non-stationary spatio-temporal processes. We propose spatially structured
Vector Autoregressions (VARs) for modelling the process between changepoints
(CPs) and give an upper bound on the approximation error of such models. The
resulting algorithm performs prediction, model selection and CP detection
on-line. Its time complexity is linear and its space complexity constant, and
thus it is two orders of magnitudes faster than its closest competitor. In
addition, it outperforms the state of the art for multivariate data.Comment: 10 pages, 7f figures, to appear in Proceedings of the 35th
International Conference on Machine Learning 201
Robust Bayesian Inference for Measurement Error Models
Measurement error occurs when a set of covariates influencing a response
variable are corrupted by noise. This can lead to misleading inference
outcomes, particularly in problems where accurately estimating the relationship
between covariates and response variables is crucial, such as causal effect
estimation. Existing methods for dealing with measurement error often rely on
strong assumptions such as knowledge of the error distribution or its variance
and availability of replicated measurements of the covariates. We propose a
Bayesian Nonparametric Learning framework which is robust to mismeasured
covariates, does not require the preceding assumptions, and is able to
incorporate prior beliefs about the true error distribution. Our approach gives
rise to two methods that are robust to measurement error via different loss
functions: one based on the Total Least Squares objective and the other based
on Maximum Mean Discrepancy (MMD). The latter allows for generalisation to
non-Gaussian distributed errors and non-linear covariate-response
relationships. We provide bounds on the generalisation error using the MMD-loss
and showcase the effectiveness of the proposed framework versus prior art in
real-world mental health and dietary datasets that contain significant
measurement errors.Comment: 43 pages, 4 figure
Doubly robust Bayesian inference for non-stationary streaming data with β-divergences
We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with β-divergences. The resulting inference procedure is doubly robust for both the predictive and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a construction for exponential models and demonstrate it on the Bayesian Linear Regression model. In so doing, we make two additional contributions: Firstly, we make GBI scalable using Structural Variational approximations that are exact as β→0 . Secondly, we give a principled way of choosing the divergence parameter β by minimizing expected predictive loss on-line. We offer the state of the art and improve the False Discovery Rate of CP S by more than 80% on real world data
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