6 papers · 1 filter
Doubly Outlier-Robust Online Infinite Hidden Markov Model
Horace Yiu, Leandro Sánchez-Betancourt, Álvaro Cartea +1
We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is misspecified. Leveraging recent adv…
A Predictive View on Streaming Hidden Markov Models
Gerardo Duran-Martin
We develop a predictive-first optimisation framework for streaming hidden Markov models. Unlike classical approaches that prioritise full posterior recovery under a fully specified…
Adaptive, Robust and Scalable Bayesian Filtering for Online Learning
Gerardo Duran-Martin
In this thesis, we introduce Bayesian filtering as a principled framework for tackling diverse sequential machine learning problems, including online (continual) learning, prequent…
A unifying framework for generalised Bayesian online learning in non-stationary environments
Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Alexander Y. Shestopaloff +1
We propose a unifying framework for methods that perform probabilistic online learning in non-stationary environments. We call the framework BONE, which stands for generalised (B)a…
Outlier-robust Kalman Filtering through Generalised Bayes
Gerardo Duran-Martin, Matias Altamirano, Alexander Y. Shestopaloff +5
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models.…
Low-rank extended Kalman filtering for online learning of neural networks from streaming data
Peter G. Chang, Gerardo Durán-Martín, Alexander Y Shestopaloff +2
We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream. The met…