57 citations · 223 across the 23 of their papers we have counts for
24 papers
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer +7
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete…
Methods and applications of PDMP samplers with boundary conditions
Joris Bierkens, Sebastiano Grazzi, Gareth Roberts +1
We extend Monte Carlo samplers based on piecewise deterministic Markov processes (PDMP samplers) by formally defining different boundary conditions such as sticky floors, soft and…
Compositionality in algorithms for smoothing
Moritz Schauer, Frank van der Meulen, Andi Q. Wang
Backward Filtering Forward Guiding (BFFG) is a bidirectional algorithm proposed in Mider et al. [2021] and studied more in depth in a general setting in Van der Meulen and Schauer…
Automatic Differentiation of Programs with Discrete Randomness
Gaurav Arya, Moritz Schauer, Frank Schäfer +1
Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing…
Flexible Group Fairness Metrics for Survival Analysis
Raphael Sonabend, Florian Pfisterer, Alan Mishler +4
Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorit…
Conditioning continuous-time Markov processes by guiding
Marc Corstanje, Frank van der Meulen, Moritz Schauer
A continuous-time Markov process can be conditioned to be in a given state at a fixed time using Doob's -transform. This transform requires the typically intractable…