Bayesian Active Learning for Classification and Preference Learning - Neil Houlsby; Ferenc Huszar; Zoubin Ghahramani; Máté Lengyel (CoRR, 2011)

Approximate inference for the loss-calibrated Bayesian. - Simon Lacoste-Julien; Ferenc Huszar; Zoubin Ghahramani (AISTATS, 2011)

Collaborative Gaussian Processes for Preference Learning. - Neil Houlsby; José Miguel Hernández-Lobato; Ferenc Huszar; Zoubin Ghahramani (NIPS, 2012)

Optimally-Weighted Herding is Bayesian Quadrature. - Ferenc Huszar; David Duvenaud (UAI, 2012)

How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? - Ferenc Huszar (CoRR, 2015)

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 (CVPR, 2016)

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. - Christian Ledig; Lucas Theis; Ferenc Huszar; Jose Caballero; Andrew P. Aitken; Alykhan Tejani; Johannes Totz; Zehan Wang; Wenzhe Shi (CoRR, 2016)

Is the deconvolution layer the same as a convolutional layer? - Wenzhe Shi; Jose Caballero; Lucas Theis; Ferenc Huszar; Andrew P. Aitken; Christian Ledig; Zehan Wang (CoRR, 2016)

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

Variational Inference using Implicit Distributions. - Ferenc Huszár (CoRR, 2017)

On Quadratic Penalties in Elastic Weight Consolidation. - Ferenc Huszár (CoRR, 2017)

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

Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices. - Katherine Storrs; Sebastiaan Van Leuven; Steve Kojder; Lucas Theis; Ferenc Huszar (PCS, 2018)

Faster gaze prediction with dense networks and Fisher pruning. - Lucas Theis; Iryna Korshunova; Alykhan Tejani; Ferenc Huszár (CoRR, 2018)

Addressing delayed feedback for continuous training with neural networks in CTR prediction. - Sofia Ira Ktena; Alykhan Tejani; Lucas Theis; Pranay Kumar Myana; Deepak Dilipkumar; Ferenc Huszár; Steven Yoo; Wenzhe Shi (RecSys, 2019)

Deep Bayesian Bandits: Exploring in Online Personalized Recommendations. - Dalin Guo; Sofia Ira Ktena; Pranay Kumar Myana; Ferenc Huszar; Wenzhe Shi; Alykhan Tejani; Michael Kneier; Sourav Das (RecSys, 2020)

Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. - Caojin Zhang; Yicun Liu; Yuanpu Xie; Sofia Ira Ktena; Alykhan Tejani; Akshay Gupta; Pranay Kumar Myana; Deepak Dilipkumar; Suvadip Paul; Ikuhiro Ihara; Prasang Upadhyaya; Ferenc Huszar; et al. (RecSys, 2020)

Deep Bayesian Bandits: Exploring in Online Personalized Recommendations. - Dalin Guo; Sofia Ira Ktena; Ferenc Huszar; Pranay Kumar Myana; Wenzhe Shi; Alykhan Tejani (CoRR, 2020)

Efficient Wasserstein Natural Gradients for Reinforcement Learning. - Ted Moskovitz; Michael Arbel; Ferenc Huszar; Arthur Gretton (ICLR, 2021)

Depth Without the Magic: Inductive Bias of Natural Gradient Descent. - Anna Kerekes; Anna Mészáros; Ferenc Huszár (CoRR, 2021)

Rethinking Sharpness-Aware Minimization as Variational Inference. - Szilvia Ujváry; Zsigmond Telek; Anna Kerekes; Anna Mészáros; Ferenc Huszár (CoRR, 2022)

Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics. - Tomo Lazovich; Luca Belli; Aaron Gonzales; Amanda Bower; Uthaipon Tantipongpipat; Kristian Lum; Ferenc Huszár; Rumman Chowdhury (Patterns, 2022)

Algorithmic amplification of politics on Twitter. - Ferenc Huszár; Sofia Ira Ktena; Conor O'Brien; Luca Belli; Andrew Schlaikjer; Moritz Hardt (Proc. Natl. Acad. Sci. USA, 2022)

nl-causal-representations. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Zenodo, 2022)

Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data. - Siyuan Guo; Viktor Tóth; Bernhard Schölkopf; Ferenc Huszar (NeurIPS, 2023)

FedL2P: Federated Learning to Personalize. - Royson Lee; Minyoung Kim 001; Da Li 001; Xinchi Qiu; Timothy M. Hospedales; Ferenc Huszar; Nicholas D. Lane (NeurIPS, 2023)

Jacobian-based Causal Discovery with Nonlinear ICA. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Trans. Mach. Learn. Res., 2023)

nl-causal-representations. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Zenodo, 2023)

Recurrent Early Exits for Federated Learning with Heterogeneous Clients. - Royson Lee; Javier Fernández-Marqués; Shell Xu Hu; Da Li 001; Stefanos Laskaridis; Lukasz Dudziak; Timothy M. Hospedales; Ferenc Huszár; Nicholas Donald Lane (ICML, 2024)

Position: Understanding LLMs Requires More Than Statistical Generalization. - Patrik Reizinger; Szilvia Ujváry; Anna Mészáros; Anna Kerekes; Wieland Brendel; Ferenc Huszár (ICML, 2024)

Do Finetti: On Causal Effects for Exchangeable Data. - Siyuan Guo; Chi Zhang; Karthika Mohan; Ferenc Huszar; Bernhard Schölkopf (NeurIPS, 2024)

Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts. - Anna Mészáros; Szilvia Ujváry; Wieland Brendel; Patrik Reizinger; Ferenc Huszar (NeurIPS, 2024)

Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation. - Royson Lee; Rui Li 052; Stylianos I. Venieris; Timothy M. Hospedales; Ferenc Huszár; Nicholas D. Lane (WACV, 2024)

Understanding LLMs Requires More Than Statistical Generalization. - Patrik Reizinger; Szilvia Ujváry; Anna Mészáros; Anna Kerekes; Wieland Brendel; Ferenc Huszár (CoRR, 2024)

Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs. - Siyuan Guo; Aniket Didolkar; Nan Rosemary Ke; Anirudh Goyal; Ferenc Huszár; Bernhard Schölkopf (CoRR, 2024)

Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts. - Anna Mészáros; Szilvia Ujváry; Wieland Brendel; Patrik Reizinger; Ferenc Huszár (CoRR, 2024)

Beyond the Boundaries of Proximal Policy Optimization. - Charlie B. Tan; Edan Toledo; Benjamin Ellis; Jakob N. Foerster; Ferenc Huszár (CoRR, 2024)

In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate? - Ben Bucknall; Saad Siddiqui; Lara Thurnherr; Conor McGurk; Ben Harack; Anka Reuel; Patricia Paskov; Casey Mahoney; Sören Mindermann; Scott Singer; Vinay Hiremath; Charbel-Raphaël Ségerie; et al. (FAccT, 2025)

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

In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate? - Ben Bucknall; Saad Siddiqui; Lara Thurnherr; Conor McGurk; Ben Harack; Anka Reuel; Patricia Paskov; Casey Mahoney; Sören Mindermann; Scott Singer; Vinay Hiremath; Charbel-Raphaël Ségerie; et al. (CoRR, 2025)