Generalised GPLVM with Stochastic Variational Inference. - Vidhi Lalchand; Aditya Ravuri; Neil D. Lawrence (AISTATS, 2022)

Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. - Vidhi Lalchand; Aditya Ravuri; Emma Dann; Natsuhiko Kumasaka; Dinithi Sumanaweera; Rik G. H. Lindeboom; Shaista Madad; Sarah A. Teichmann; Neil D. Lawrence (MLCB, 2022)

Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. - Vidhi Lalchand; Aditya Ravuri; Neil D. Lawrence (CoRR, 2022)

Ice Core Dating using Probabilistic Programming. - Aditya Ravuri; Tom R. Andersson; Ieva Kazlauskaite; Will Tebbutt; Richard E. Turner; J. Scott Hosking; Neil D. Lawrence; Markus Kaiser 001 (CoRR, 2022)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Bojana Rankovic; Yuanqi Du; Arian R. Jamasb; Julius Schwartz; Austin Tripp; Gregory Kell; Anthony Bourached; et al. (CoRR, 2022)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Yuanqi Du; Samuel Stanton; Gary Tom; Bojana Rankovic; Arian Rokkum Jamasb; Aryan Deshwal; Julius Schwartz; et al. (NeurIPS, 2023)

Dimensionality Reduction as Probabilistic Inference. - Aditya Ravuri; Francisco Vargas 001; Vidhi Lalchand; Neil D. Lawrence (CoRR, 2023)

Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction. - Aditya Ravuri; Erica Cooper; Junichi Yamagishi (ICASSP Workshops, 2024)

Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. - Sarah Zhao; Aditya Ravuri; Vidhi Lalchand; Neil D. Lawrence (CoRR, 2024)

Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE. - Aditya Ravuri; Neil D. Lawrence (CoRR, 2024)

On Feature Learning for Titi Monkey Activity Detection. - Aditya Ravuri; Jen Muir; Neil D. Lawrence (CoRR, 2024)

The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. - Peter Mostowsky; Vincent Dutordoir; Iskander Azangulov; Noémie Jaquier; Michael John Hutchinson; Aditya Ravuri; Leonel Rozo; Alexander Terenin; Viacheslav Borovitskiy (CoRR, 2024)

Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models. - Konstantin Donhauser; Kristina Ulicna; Gemma E. Moran; Aditya Ravuri; Kian Kenyon-Dean; Cian Eastwood; Jason S. Hartford (CoRR, 2024)

Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout. - Aditya Ravuri; Neil D. Lawrence (CoRR, 2025)

Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps: An Interpretation and Potential Improvements. - Aditya Ravuri; Neil D. Lawrence (CoRR, 2025)