8 papers
Predictively-Oriented Kalman Filtering
Zheyang Shen, Gerardo Duran-Martin, Chris. J. Oates
This paper presents a post-Bayesian approach to online filtering in nonlinear state-space models, capable of avoiding over-confident inferences in settings where either the dynamic…
Doubly Outlier-Robust Online Infinite Hidden Markov Model
Horace Yiu, Leandro Sánchez-Betancourt, Ãlvaro 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…
Detecting Toxic Flow
Ãlvaro Cartea, Gerardo Duran-Martin, Leandro Sánchez-Betancourt
This paper develops a framework to predict toxic trades that a broker receives from her clients. Toxic trades are predicted with a novel online learning Bayesian method which we ca…
Martingale Posterior Neural Networks for Fast Sequential Decision Making
Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Ãlvaro Cartea +1
We introduce scalable algorithms for online learning of neural network parameters and Bayesian sequential decision making. Unlike classical Bayesian neural networks, which induce p…
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…