30 citations · 91 across the 16 of their papers we have counts for
18 papers
Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2
Latent Labs Team, Henry Kenlay, Daniella Pretorius +15
Drug discovery has long sought computational systems capable of designing drug-like molecules directly: developable and non-immunogenic from the start. Here we introduce Latent-X2,…
Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design
Latent Labs Team, Alex Bridgland, Jonathan Crabbé +13
Traditional drug discovery relies on rounds of screening millions of candidate molecules with low success rates, making drug discovery time and resource intensive. To overcome this…
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
Yan Wu, Esther Wershof, Sebastian M Schmon +7
We introduce a comprehensive framework for modeling single cell transcriptomic responses to perturbations, aimed at standardizing benchmarking in this rapidly evolving field. Our a…
Robust Neural Posterior Estimation and Statistical Model Criticism
Daniel Ward, Patrick Cannon, Mark Beaumont +2
Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes…
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
Patrick Cannon, Daniel Ward, Sebastian M. Schmon
Aided by advances in neural density estimation, considerable progress has been made in recent years towards a suite of simulation-based inference (SBI) methods capable of performin…
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning
Jordan Langham-Lopez, Sebastian M. Schmon, Patrick Cannon
Multi-agent reinforcement learning experiments and open-source training environments are typically limited in scale, supporting tens or sometimes up to hundreds of interacting agen…
Calibrating Agent-based Models to Microdata with Graph Neural Networks
Joel Dyer, Patrick Cannon, J. Doyne Farmer +1
Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose. In recent years, simulation-based inferenc…
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Joel Dyer, Patrick Cannon, Sebastian M Schmon
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference…
Black-box Bayesian inference for economic agent-based models
Joel Dyer, Patrick Cannon, J. Doyne Farmer +1
Simulation models, in particular agent-based models, are gaining popularity in economics. The considerable flexibility they offer, as well as their capacity to reproduce a variety…
Learning Multimodal VAEs through Mutual Supervision
Tom Joy, Yuge Shi, Philip H. S. Torr +3
Multimodal VAEs seek to model the joint distribution over heterogeneous data (e.g.\ vision, language), whilst also capturing a shared representation across such modalities. Prior w…
Approximate Bayesian Computation with Path Signatures
Joel Dyer, Patrick Cannon, Sebastian M Schmon
Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation generates likelihood-free post…
Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics
Sebastian M Schmon, Philippe Gagnon
High-dimensional limit theorems have been shown useful to derive tuning rules for finding the optimal scaling in random-walk Metropolis algorithms. The assumptions under which weak…
Generalized Posteriors in Approximate Bayesian Computation
Sebastian M Schmon, Patrick W Cannon, Jeremias Knoblauch
Complex simulators have become a ubiquitous tool in many scientific disciplines, providing high-fidelity, implicit probabilistic models of natural and social phenomena. Unfortunate…
Capturing Label Characteristics in VAEs
Tom Joy, Sebastian M. Schmon, Philip H. S. Torr +2
We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels. While prior work has typically conf…
A General Framework for Survival Analysis and Multi-State Modelling
Stefan Groha, Sebastian M Schmon, Alexander Gusev
Survival models are a popular tool for the analysis of time to event data with applications in medicine, engineering, economics, and many more. Advances like the Cox proportional h…
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
Jack K Fitzsimons, Sebastian M Schmon, Stephen J Roberts
Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties l…
Bernoulli Race Particle Filters
Sebastian M Schmon, Arnaud Doucet, George Deligiannidis
When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed. To circumvent this problem state-of-the-art algorithms replace…
Large Sample Asymptotics of the Pseudo-Marginal Method
Sebastian M. Schmon, George Deligiannidis, Arnaud Doucet +1
The pseudo-marginal algorithm is a variant of the Metropolis--Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate u…