Publications (17)
Quasi-Newton Sequential Monte Carlo
Samuel Duffield, Sumeetpal S. Singh
Sequential Monte Carlo samplers represent a compelling approach to posterior inference in Bayesian models, due to being parallelisable and providing an unbiased estimate of the pos…
Lattice Random Walk Discretisations of Stochastic Differential Equations
Samuel Duffield, Maxwell Aifer, Denis Melanson +2
We introduce a lattice random walk discretisation scheme for stochastic differential equations (SDEs) that samples binary or ternary increments at each step, suppressing complex dr…
A State-Space Perspective on Modelling and Inference for Online Skill Rating
Samuel Duffield, Samuel Power, Lorenzo Rimella
We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and di…
Scalable Thermodynamic Second-order Optimization
Kaelan Donatella, Samuel Duffield, Denis Melanson +7
Many hardware proposals have aimed to accelerate inference in AI workloads. Less attention has been paid to hardware acceleration of training, despite the enormous societal impact…
Thermodynamic Bayesian Inference
Maxwell Aifer, Samuel Duffield, Kaelan Donatella +6
A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks…
Exploiting Inductive Biases in Video Modeling through Neural CDEs
Johnathan Chiu, Samuel Duffield, Max Hunter-Gordon +3
We introduce a novel approach to video modeling that leverages controlled differential equations (CDEs) to address key challenges in video tasks, notably video interpolation and ma…