Bruno Mlodozeniec

According to our database, Bruno Mlodozeniec authored at least 21 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

The Design Space of Tri-Modal Masked Diffusion Models.

Louis Béthune, Victor Turrisi, Bruno Kacper Mlodozeniec, Pau Rodríguez López, Lokesh Boominathan, Nikhil Bhendawade, Amitis Shidani, Joris Pelemans, Theo X. Olausson, R. Devon Hjelm, Paul Dixon, João Monteiro, Pierre Ablin, Vishnu Banna, Arno Blaas, Nick Henderson, Kari Noriy, Dan Busbridge, Joshua M. Susskind, Marco Cuturi, Irina Belousova, Luca Zappella, Russ Webb, Jason Ramapuram

CoRR, February, 2026

Incremental Transformer Neural Processes.

Philip Mortimer, Cristiana Diaconu, Tommy Rochussen, Bruno Mlodozeniec, Richard E. Turner

CoRR, February, 2026

2025

Probabilistic Modelling is Sufficient for Causal Inference.

Bruno Mlodozeniec, David Krueger, Richard E. Turner

CoRR, December, 2025

Completed Hyperparameter Transfer across Modules, Width, Depth, Batch and Duration.

Bruno Mlodozeniec, Pierre Ablin, Louis Béthune, Dan Busbridge, Michal Klein, Jason Ramapuram, Marco Cuturi

CoRR, December, 2025

Better Hessians Matter: Studying the Impact of Curvature Approximations in Influence Functions.

Steve Hong, Runa Eschenhagen, Bruno Mlodozeniec, Richard E. Turner

CoRR, September, 2025

Wavelet-Induced Rotary Encodings: RoPE Meets Graphs.

Isaac Reid, Arijit Sehanobish, Cederik Höfs, Bruno Mlodozeniec, Leonhard Vulpius, Federico Barbero, Adrian Weller, Krzysztof Choromanski, Richard E. Turner, Petar Velickovic

CoRR, September, 2025

SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges.

Dewi S. W. Gould, Bruno Mlodozeniec, Samuel F. Brown

CoRR, August, 2025

Distributional Training Data Attribution.

Bruno Mlodozeniec, Isaac Reid, Sam Power, David Krueger, Murat A. Erdogdu, Richard E. Turner, Roger B. Grosse

CoRR, June, 2025

Position: Probabilistic Modelling is Sufficient for Causal Inference.

Bruno Kacper Mlodozeniec, David Krueger, Richard E. Turner

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Influence Functions for Scalable Data Attribution in Diffusion Models.

Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes.

Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, José Miguel Hernández-Lobato

CoRR, 2024

Denoising Diffusion Probabilistic Models in Six Simple Steps.

Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya, Andrew Y. K. Foong, Bruno Mlodozeniec

CoRR, 2024

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes.

Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

A Generative Model of Symmetry Transformations.

James Urquhart Allingham, Bruno Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Implicit meta-learning may lead language models to trust more reliable sources.

Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Kacper Mlodozeniec, Tegan Maharaj, David Krueger

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Implicitly Bayesian Prediction Rules in Deep Learning.

Bruno Mlodozeniec, David Krueger, Richard E. Turner

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2024

2023

Meta- (out-of-context) learning in neural networks.

Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Mlodozeniec, David Krueger

CoRR, 2023

What Mechanisms Does Knowledge Distillation Distill?

Cindy Wu, Ekdeep Singh Lubana, Bruno Kacper Mlodozeniec, Robert Kirk, David Krueger

Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 2023

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics.

Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hyperparameter Optimization through Neural Network Partitioning.

Bruno Mlodozeniec, Matthias Reisser, Christos Louizos

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2020

Ensemble Distribution Distillation.

Andrey Malinin, Bruno Mlodozeniec, Mark J. F. Gales

Proceedings of the 8th International Conference on Learning Representations, 2020