Bálint Mucsányi

According to our database, Bálint Mucsányi authored at least 13 papers between 2021 and 2025.

Collaborative distances:

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

2025

laplax - Laplace Approximations with JAX.

Tobias Weber, Bálint Mucsányi, Lenard Rommel, Thomas Christie, Lars Kasüschke, Marvin Pförtner, Philipp Hennig

CoRR, July, 2025

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning.

Patrik Reizinger, Bálint Mucsányi, Siyuan Guo, Benjamin Eysenbach, Bernhard Schölkopf, Wieland Brendel

CoRR, July, 2025

Logit Reweighting for Topic-Focused Summarization.

Joschka Braun, Bálint Mucsányi, Seyed Ali Bahrainian

CoRR, July, 2025

Kronecker-factored Approximate Curvature (KFAC) From Scratch.

Felix Dangel, Bálint Mucsányi, Tobias Weber, Runa Eschenhagen

CoRR, July, 2025

Geometric Gaussian Approximations of Probability Distributions.

Nathaël Da Costa, Bálint Mucsányi, Philipp Hennig

CoRR, July, 2025

sbi reloaded: a toolkit for simulation-based inference workflows.

Jan Boelts, Michael Deistler, Manuel Glöckler, Álvaro Tejero-Cantero, Jan-Matthis Lueckmann, Guy Moss, Peter Steinbach, Thomas Moreau, Fabio Muratore, Julia Linhart, Conor Durkan, Julius Vetter, Benjamin Kurt Miller, Maternus Herold, Abolfazl Ziaeemehr, Matthijs Pals, Theo Gruner, Sebastian Bischoff, Nastya Krouglova, Richard Gao, Janne K. Lappalainen, Bálint Mucsányi, Felix Pei, Auguste Schulz, Zinovia Stefanidi, Pedro Rodrigues, Cornelius Schröder, Faried Abu Zaid, Jonas Beck, Jaivardhan Kapoor, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke

J. Open Source Softw., May, 2025

Rethinking Approximate Gaussian Inference in Classification.

Bálint Mucsányi, Nathaël Da Costa, Philipp Hennig

CoRR, February, 2025

Debiasing Mini-Batch Quadratics for Applications in Deep Learning.

Lukas Tatzel, Bálint Mucsányi, Osane Hackel, Philipp Hennig

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks.

Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Trustworthy Machine Learning.

Bálint Mucsányi, Michael Kirchhof, Elisa Nguyen, Alexander Rubinstein, Seong Joon Oh

CoRR, 2023

URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates.

Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh, Enkelejda Kasneci

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

2022

Flexible Example-Based Program Synthesis on Tree-Structured Function Compositions.

Bálint Mucsányi, Bálint Gyarmathy, Ádám Czapp, Balázs Pintér

SN Comput. Sci., 2022

2021

Flexcoder: Practical Program Synthesis with Flexible Input Lengths and Expressive Lambda Functions.

Bálint Gyarmathy, Bálint Mucsányi, Ádám Czapp, Dávid Szilágyi, Balázs Pintér

Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods, 2021