Adam Golinski

According to our database, Adam Golinski authored at least 16 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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, May, 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, May, 2025

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, April, 2025

2024

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

2023

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

Proceedings of the International Conference on Machine Learning, 2023

2022

COIN++: Neural Compression Across Modalities.

Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet

Trans. Mach. Learn. Res., 2022

COIN++: Data Agnostic Neural Compression.

Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet

CoRR, 2022

Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently.

Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth

Proceedings of the Uncertainty in Artificial Intelligence, 2022

LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood.

Piotr Tempczyk, Rafal Michaluk, Lukasz Garncarek, Przemyslaw Spurek, Jacek Tabor, Adam Golinski

Proceedings of the International Conference on Machine Learning, 2022

2021

Expectation Programming.

Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth

CoRR, 2021

COIN: COmpression with Implicit Neural representations.

Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet

CoRR, 2021

2020

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators.

Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi

J. Mach. Learn. Res., 2020

Feedback Recurrent Autoencoder for Video Compression.

Adam Golinski, Reza Pourreza, Yang Yang, Guillaume Sautière, Taco S. Cohen

Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

2019

Amortized Monte Carlo Integration.

Adam Golinski, Frank Wood, Tom Rainforth

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Faithful Inversion of Generative Models for Effective Amortized Inference.

Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference.

Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, Frank D. Wood

CoRR, 2017