[slides] Adaptive, robust, and scalable Bayesian filtering for online learning

Online learning and sequential decision making Observe sequence of features $\vx_t\in\reals^M$ and observations $\vy_t \in \reals^o$: $$ {\cal D}_{1:t} = \{(\vx_1, \vy_1), \ldots, (\vx_t, \vy_t)\}. $$ We model a mapping from $\vx_t$ to $\vy_t$ through a function $h: \Theta \times \reals^M \to \reals^o$ that maps model parameters $\vtheta \in \Theta$ and features $\vx_t \in \reals^M$ to outputs…