Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. - Stefan Webb; Adam Golinski; Robert Zinkov; N. Siddharth 001; Yee Whye Teh; Frank D. Wood (CoRR, 2017)

Faithful Inversion of Generative Models for Effective Amortized Inference. - Stefan Webb; Adam Golinski; Robert Zinkov; Siddharth Narayanaswamy; Tom Rainforth; Yee Whye Teh; Frank Wood (NeurIPS, 2018)

Amortized Monte Carlo Integration. - Adam Golinski; Frank Wood; Tom Rainforth (ICML, 2019)

Feedback Recurrent Autoencoder for Video Compression. - Adam Golinski; Reza Pourreza 002; Yang Yang 010; Guillaume Sautière; Taco S. Cohen (ACCV (4), 2020)

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators. - Tom Rainforth; Adam Golinski; Frank Wood; Sheheryar Zaidi (J. Mach. Learn. Res., 2020)

COIN: COmpression with Implicit Neural representations. - Emilien Dupont; Adam Golinski; Milad Alizadeh; Yee Whye Teh; Arnaud Doucet (CoRR, 2021)

Expectation Programming. - Tim Reichelt; Adam Golinski; Luke Ong; Tom Rainforth (CoRR, 2021)

LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood. - Piotr Tempczyk; Rafal Michaluk; Lukasz Garncarek; Przemyslaw Spurek; Jacek Tabor; Adam Golinski (ICML, 2022)

Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently. - Tim Reichelt; Adam Golinski; Luke Ong; Tom Rainforth (UAI, 2022)

COIN++: Data Agnostic Neural Compression. - Emilien Dupont; Hrushikesh Loya; Milad Alizadeh; Adam Golinski; Yee Whye Teh; Arnaud Doucet (CoRR, 2022)

COIN++: Neural Compression Across Modalities. - Emilien Dupont; Hrushikesh Loya; Milad Alizadeh; Adam Golinski; Yee Whye Teh; Arnaud Doucet (Trans. Mach. Learn. Res., 2022)

The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning. - Borja Rodríguez Gálvez; Arno Blaas; Pau Rodríguez; Adam Golinski; Xavier Suau; Jason Ramapuram; Dan Busbridge; Luca Zappella (ICML, 2023)

Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare. - Arno Blaas; Adam Golinski; Andrew C. Miller; Luca Zappella; Jörn-Henrik Jacobsen; Christina Heinze-Deml (CoRR, 2024)

Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results. - Andrea Santilli; Adam Golinski; Michael Kirchhof; Federico Danieli; Arno Blaas; Miao Xiong; Luca Zappella; Sinead Williamson (CoRR, 2025)

Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution? - Michael Kirchhof; Luca Füger; Adam Golinski; Eeshan Gunesh Dhekane; Arno Blaas; Sinead Williamson (CoRR, 2025)

Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs. - Yinong Oliver Wang; Nivedha Sivakumar; Falaah Arif Khan; Rin Metcalf Susa; Adam Golinski; Natalie Mackraz; Barry-John Theobald; Luca Zappella; Nicholas Apostoloff (CoRR, 2025)