Huszar, Ferenc

24 publications

ICLR 2025 Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel

ICLRW 2025 Implicit Bayesian Inference Is an Insufficient Explanation of Language Model Behaviour in Compositional Tasks Szilvia Ujváry, Anna Mészáros, Wieland Brendel, Patrik Reizinger, Ferenc Huszár

NeurIPS 2024 Do Finetti: On Causal Effects for Exchangeable Data Siyuan Guo, Chi Zhang, Karthika Mohan, Ferenc Huszár, Bernhard Schölkopf

NeurIPSW 2024 Explicit Regularisation, Sharpness and Calibration Israel Mason-Williams, Fredrik Ekholm, Ferenc Huszár

WACV 2024 Meta-Learned Kernel for Blind Super-Resolution Kernel Estimation Royson Lee, Rui Li, Stylianos Venieris, Timothy Hospedales, Ferenc Huszár, Nicholas D. Lane

ICMLW 2024 Parallelising Differentiable Algorithms Removes the Scalar Bottleneck: A Case Study Euan Ong, Ferenc Huszár, Pietro Lio, Petar Veličković

ICML 2024 Position: Understanding LLMs Requires More than Statistical Generalization Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár

ICML 2024 Recurrent Early Exits for Federated Learning with Heterogeneous Clients Royson Lee, Javier Fernandez-Marques, Shell Xu Hu, Da Li, Stefanos Laskaridis, Łukasz Dudziak, Timothy Hospedales, Ferenc Huszár, Nicholas Donald Lane

NeurIPS 2024 Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts Anna Mészáros, Szilvia Ujváry, Wieland Brendel, Patrik Reizinger, Ferenc Huszár

NeurIPSW 2024 Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts Anna Mészáros, Szilvia Ujváry, Wieland Brendel, Patrik Reizinger, Ferenc Huszár

NeurIPS 2023 Causal De Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data Siyuan Guo, Viktor Toth, Bernhard Schölkopf, Ferenc Huszar

NeurIPS 2023 FedL2P: Federated Learning to Personalize Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy Hospedales, Ferenc Huszar, Nicholas Lane

TMLR 2023 Jacobian-Based Causal Discovery with Nonlinear ICA Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel

NeurIPSW 2022 Rethinking Sharpness-Aware Minimization as Variational Inference Szilvia Ujváry, Zsigmond Telek, Anna Kerekes, Anna Mészáros, Ferenc Huszár

ICLR 2021 Efficient Wasserstein Natural Gradients for Reinforcement Learning Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton

ICMLW 2020 Exchangeable Models in Meta Reinforcement Learning Iryna Korshunova, Jonas Degrave, Joni Dambre, Arthur Gretton, Ferenc Huszar

NeurIPS 2018 BRUNO: A Deep Recurrent Model for Exchangeable Data Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre

ICLR 2017 Amortised MAP Inference for Image Super-Resolution Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár

ICLR 2017 Lossy Image Compression with Compressive Autoencoders Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár

CVPR 2017 Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi

CVPR 2016 Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang

NeurIPS 2012 Collaborative Gaussian Processes for Preference Learning Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Jose M. Hernández-lobato

UAI 2012 Optimally-Weighted Herding Is Bayesian Quadrature Ferenc Huszar, David Duvenaud

AISTATS 2011 Approximate Inference for the Loss-Calibrated Bayesian Simon Lacoste–Julien, Ferenc Huszár, Zoubin Ghahramani