Archive
2024
2023
· | Poster · NeurIPS 2023 · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent | |
· | Poster · NeurIPS 2023 · Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds | |
· | Poster · NeurIPS Dataset Track 2023 · The Cambridge Law Corpus: A Dataset for Legal AI Research | |
· | Talk · Université Grenoble Alpes · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent | |
· | Talk · ETH Zürich · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent | |
· | Talk · University College London · Physically Structured Neural Networks for Smooth and Contact Dynamics | |
· | Talk · Vector Institute · Physically Structured Neural Networks for Smooth and Contact Dynamics | |
· | Talk · University of Tübingen · Pathwise Conditioning and Non-Euclidean Gaussian Processes | |
· | Talk · Lancaster University · Pathwise Conditioning and Non-Euclidean Gaussian Processes |
2022
2021
2020
· | Poster · NeurIPS 2020 · Matérn Gaussian Processes on Riemannian Manifolds | |
· | Talk · EMNLP 2020 · Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models | |
· | Talk · TheAlgo 2020 · Matérn Gaussian Processes on Riemannian Manifolds | |
· | Talk · University of Oxford · Pathwise, spectral, and geometric perspectives on Gaussian processes | |
· | Talk · Imperial College London · Efficiently sampling functions from Gaussian process posteriors | |
· | Talk · Symposium on Machine Learning and Dynamical Systems 2020 · Efficiently sampling functions from Gaussian process posteriors | |
· | Talk · Imperial College London · Efficiently sampling functions from Gaussian process posteriors | |
· | Talk · GE Research · Pathwise, spectral, and geometric perspectives on Gaussian processes | |
· | Talk · AISTATS 2020 · Asynchronous Gibbs Sampling | |
· | Talk · University College London · Towards physically structured probabilistic reinforcement learning | |
· | Talk · PROWLER.io · Towards physically structured probabilistic reinforcement learning |
2019
· | Talk · Linköping Workshop on Computational Text Analysis 2019 · Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models | |
· | Poster · Workshop on Structural Inference in High-Dimensional Models 2019 · A Piecewise Deterministic Markov Process via (r, θ) swaps in hyperspherical coordinates |
2018
· | Talk · CRISM Summer School 2018 · Pólya Urn Latent Dirichlet Allocation | |
· | Talk · MaxEnt 2018 · A Piecewise Deterministic Markov Process via (r, θ) swaps in hyperspherical coordinates | |
· | Poster · ISBA 2018 · Pólya Urn Latent Dirichlet Allocation | |
· | Poster · Gregynog Statistical Conference 2018 · Pólya Urn Latent Dirichlet Allocation | |
· | Talk · Carnegie Mellon University · Pólya Urn Latent Dirichlet Allocation |
2013
· | Undergraduate Research Poster · SHAPE |
2024
2023
2022
· | PhD Thesis · Gaussian Processes and Statistical Decision-making in Non-Euclidean spaces |
2021
2020
· | JMLR 2021 · Pathwise Conditioning of Gaussian Processes | |
· | AISTATS 2021 · Matérn Gaussian Processes on Graphs | |
· | NeurIPS 2020 · Matérn Gaussian Processes on Riemannian Manifolds | |
· | ICML 2020 · Efficiently Sampling Functions from Gaussian Process Posteriors | |
· | AISTATS 2021 · Aligning Time Series on Incomparable Spaces | |
· | AISTATS 2020 · Variational Integrator Networks for Physically Structured Embeddings |
2019
· | EMNLP 2020 · Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models | |
· | PAMI 2019 · Pólya Urn Latent Dirichlet Allocation |