625 citations · 2.7k across the 69 of their papers we have counts for
33 papers · 1 filter
Dimensionality Reduction as Probabilistic Inference
Aditya Ravuri, Francisco Vargas, Vidhi Lalchand +1
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data. DR is a critical st…
Ice Core Dating using Probabilistic Programming
Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite +5
Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a functio…
Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data
Sijia Li, Martín López-García, Neil D. Lawrence +1
Classically, statistical datasets have a larger number of data points than features (). The standard model of classical statistics caters for the case where data points are…
Bayesian Learning via Neural Schrödinger-Föllmer Flows
Francisco Vargas, Andrius Ovsianas, David Fernandes +3
In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i.e. Schrödinger bridges). We advocate stochastic control…
Solving Schrödinger Bridges via Maximum Likelihood
Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence +1
The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in…
Deep Gaussian Processes for Multi-fidelity Modeling
Kurt Cutajar, Mark Pullin, Andreas Damianou +2
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in o…