Scalable Structure Discovery in Regression using Gaussian Processes. - Hyunjik Kim; Yee Whye Teh (AutoML@ICML, 2016)

Tucker Gaussian Process for Regression and Collaborative Filtering. - Hyunjik Kim; Xiaoyu Lu; Seth R. Flaxman; Yee Whye Teh (CoRR, 2016)

Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes. - Hyunjik Kim; Yee Whye Teh (AISTATS, 2018)

Disentangling by Factorising. - Hyunjik Kim; Andriy Mnih (ICML, 2018)

Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects. - Adam R. Kosiorek; Hyunjik Kim; Yee Whye Teh; Ingmar Posner (NeurIPS, 2018)

Attentive Neural Processes. - Hyunjik Kim; Andriy Mnih; Jonathan Schwarz; Marta Garnelo; S. M. Ali Eslami; Dan Rosenbaum; Oriol Vinyals; Yee Whye Teh (ICLR (Poster), 2019)

Meta-Learning surrogate models for sequential decision making. - Alexandre Galashov; Jonathan Schwarz; Hyunjik Kim; Marta Garnelo; David Saxton; Pushmeet Kohli; S. M. Ali Eslami; Yee Whye Teh (CoRR, 2019)

Interpretable models in probabilistic machine learning. - Hyunjik Kim (2019)

MetaFun: Meta-Learning with Iterative Functional Updates. - Jin Xu 011; Jean-Francois Ton; Hyunjik Kim; Adam R. Kosiorek; Yee Whye Teh (ICML, 2020)

Conditional Set Generation with Transformers. - Adam R. Kosiorek; Hyunjik Kim; Danilo J. Rezende (CoRR, 2020)

LieTransformer: Equivariant Self-Attention for Lie Groups. - Michael J. Hutchinson; Charline Le Lan; Sheheryar Zaidi; Emilien Dupont; Yee Whye Teh; Hyunjik Kim (ICML, 2021)

The Lipschitz Constant of Self-Attention. - Hyunjik Kim; George Papamakarios; Andriy Mnih (ICML, 2021)

Group Equivariant Subsampling. - Jin Xu 011; Hyunjik Kim; Thomas Rainforth; Yee Whye Teh (NeurIPS, 2021)

When Does Re-initialization Work? - Sheheryar Zaidi; Tudor Berariu; Hyunjik Kim; Jörg Bornschein; Claudia Clopath; Yee Whye Teh; Razvan Pascanu (ICBINB, 2022)

From data to functa: Your data point is a function and you can treat it like one. - Emilien Dupont; Hyunjik Kim; S. M. Ali Eslami; Danilo Jimenez Rezende; Dan Rosenbaum (ICML, 2022)

From data to functa: Your data point is a function and you should treat it like one. - Emilien Dupont; Hyunjik Kim; S. M. Ali Eslami; Danilo J. Rezende; Dan Rosenbaum (CoRR, 2022)

Learning Instance-Specific Data Augmentations. - Ning Miao; Emile Mathieu; Yann Dubois; Tom Rainforth; Yee Whye Teh; Adam Foster 001; Hyunjik Kim (CoRR, 2022)

Pre-training via Denoising for Molecular Property Prediction. - Sheheryar Zaidi; Michael Schaarschmidt; James Martens; Hyunjik Kim; Yee Whye Teh; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Razvan Pascanu; Jonathan Godwin (ICLR, 2023)

Learning Instance-Specific Augmentations by Capturing Local Invariances. - Ning Miao; Tom Rainforth; Emile Mathieu; Yann Dubois; Yee Whye Teh; Adam Foster 001; Hyunjik Kim (ICML, 2023)

Spatial Functa: Scaling Functa to ImageNet Classification and Generation. - Matthias Bauer 001; Emilien Dupont; Andy Brock; Dan Rosenbaum; Jonathan Schwarz; Hyunjik Kim (CoRR, 2023)

C3: High-Performance and Low-Complexity Neural Compression from a Single Image or Video. - Hyunjik Kim; Matthias Bauer 001; Lucas Theis; Jonathan Richard Schwarz; Emilien Dupont (CVPR, 2024)

Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search. - Abbas Mehrabian; Ankit Anand; Hyunjik Kim; Nicolas Sonnerat; Matej Balog; Gheorghe Comanici; Tudor Berariu; Andrew Lee; Anian Ruoss; Anna Bulanova; Daniel Toyama; Sam Blackwell; et al. (IJCAI, 2024)

Neural Compression of Atmospheric States. - Piotr Mirowski; David Warde-Farley; Mihaela Rosca; Matthew Koichi Grimes; Yana Hasson; Hyunjik Kim; Mélanie Rey; Simon Osindero; Suman V. Ravuri; Shakir Mohamed (CoRR, 2024)

Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion. - Jona Ballé; Luca Versari; Emilien Dupont; Hyunjik Kim; Matthias Bauer (CVPR, 2025)