Andrew Y. K. Foong

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 21

Top Topics

Approximate Inference

Dempster Shafer

Top Venues

Trans. Mach. Learn. Res.

Publications

Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
Improved motif-scaffolding with SE(3) flow matching. Trans. Mach. Learn. Res. 2024 (2024)
Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
Improved motif-scaffolding with SE(3) flow matching. CoRR (2024)
Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya, Andrew Y. K. Foong, Bruno Mlodozeniec
Denoising Diffusion Probabilistic Models in Six Simple Steps. CoRR (2024)
Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. CoRR (2023)
Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
Autoregressive Conditional Neural Processes. ICLR (2023)
Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. NeurIPS (2023)
Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
Autoregressive Conditional Neural Processes. CoRR (2023)
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt
A Note on the Chernoff Bound for Random Variables in the Unit Interval. CoRR (2022)
Marcin Tomczak, Siddharth Swaroop, Andrew Y. K. Foong, Richard E. Turner
Collapsed Variational Bounds for Bayesian Neural Networks. NeurIPS (2021)
Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner
The Gaussian Neural Process. CoRR (2021)
Andrew Gordon Wilson, Pavel Izmailov, Matthew D. Hoffman, Yarin Gal, Yingzhen Li, Melanie F. Pradier, Sharad Vikram, Andrew Y. K. Foong, Sanae Lotfi, Sebastian Farquhar
Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) (2021)
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
How Tight Can PAC-Bayes be in the Small Data Regime? CoRR (2021)
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
How Tight Can PAC-Bayes be in the Small Data Regime? NeurIPS (2021)
Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. CoRR (2020)
Tim Pearce, Andrew Y. K. Foong, Alexandra Brintrup
Structured Weight Priors for Convolutional Neural Networks. CoRR (2020)
Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
Convolutional Conditional Neural Processes. ICLR (2020)
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS (2020)
Andrew Y. K. Foong, Wessel Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. NeurIPS (2020)
Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
Convolutional Conditional Neural Processes. CoRR (2019)
Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
'In-Between' Uncertainty in Bayesian Neural Networks. CoRR (2019)
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. CoRR (2019)