625 citations · 2.7k across the 69 of their papers we have counts for
6 papers · 1 filter
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…
Emulation of physical processes with Emukit
Andrei Paleyes, Mark Pullin, Maren Mahsereci +3
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical…
Behavioral Experiments for Understanding Catastrophic Forgetting
Samuel J. Bell, Neil D. Lawrence
In this paper we explore whether the fundamental tool of experimental psychology, the behavioral experiment, has the power to generate insight not only into humans and animals, but…
Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment
Corinna Cortes, Neil D. Lawrence
In this paper we revisit the 2014 NeurIPS experiment that examined inconsistency in conference peer review. We determine that 50\% of the variation in reviewer quality scores was s…
Towards better data discovery and collection with flow-based programming
Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
Despite huge successes reported by the field of machine learning, such as voice assistants or self-driving cars, businesses still observe very high failure rate when it comes to de…
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…