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2020 – today
- 2024
[c18]Royson Lee, Javier Fernández-Marqués, Shell Xu Hu, Da Li, Stefanos Laskaridis, Lukasz Dudziak, Timothy M. Hospedales, Ferenc Huszár, Nicholas Donald Lane:
Recurrent Early Exits for Federated Learning with Heterogeneous Clients. ICML 2024- [c17]Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár:
Position: Understanding LLMs Requires More Than Statistical Generalization. ICML 2024 - [c16]Royson Lee, Rui Li, Stylianos I. Venieris, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation. WACV 2024: 1485-1494 - [i26]Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár:
Understanding LLMs Requires More Than Statistical Generalization. CoRR abs/2405.01964 (2024) - [i25]Royson Lee, Javier Fernández-Marqués, Shell Xu Hu, Da Li, Stefanos Laskaridis, Lukasz Dudziak, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
Recurrent Early Exits for Federated Learning with Heterogeneous Clients. CoRR abs/2405.14791 (2024) - [i24]Siyuan Guo, Aniket Didolkar, Nan Rosemary Ke, Anirudh Goyal, Ferenc Huszár, Bernhard Schölkopf:
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs. CoRR abs/2405.15485 (2024) - [i23]Siyuan Guo, Chi Zhang, Karthika Mohan, Ferenc Huszár, Bernhard Schölkopf:
Do Finetti: On Causal Effects for Exchangeable Data. CoRR abs/2405.18836 (2024) - [i22]Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel:
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning. CoRR abs/2406.14302 (2024) - 2023
- [j4]Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszár, Rumman Chowdhury:
Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics. Patterns 4(8): 100822 (2023) - [j3]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
Jacobian-based Causal Discovery with Nonlinear ICA. Trans. Mach. Learn. Res. 2023 (2023) - [c15]Siyuan Guo, Viktor Tóth, Bernhard Schölkopf, Ferenc Huszar:
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data. NeurIPS 2023 - [c14]Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy M. Hospedales, Ferenc Huszar, Nicholas D. Lane:
FedL2P: Federated Learning to Personalize. NeurIPS 2023 - [d2]Patrik Reizinger
, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
nl-causal-representations. Version v1.0.1. Zenodo, 2023 [all versions]
- [i21]Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
FedL2P: Federated Learning to Personalize. CoRR abs/2310.02420 (2023) - 2022
- [j2]Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszár, Rumman Chowdhury:
Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics. Patterns 3(8): 100568 (2022) - [j1]Ferenc Huszár, Sofia Ira Ktena, Conor O'Brien, Luca Belli, Andrew Schlaikjer, Moritz Hardt:
Algorithmic amplification of politics on Twitter. Proc. Natl. Acad. Sci. USA 119(1): e2025334119 (2022) - [d1]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
nl-causal-representations. Version 1.0.0. Zenodo, 2022 [all versions] - [i20]Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszar, Rumman Chowdhury:
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics. CoRR abs/2202.01615 (2022) - [i19]Siyuan Guo, Viktor Tóth, Bernhard Schölkopf, Ferenc Huszár:
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data. CoRR abs/2203.15756 (2022) - [i18]Szilvia Ujváry, Zsigmond Telek, Anna Kerekes, Anna Mészáros, Ferenc Huszár:
Rethinking Sharpness-Aware Minimization as Variational Inference. CoRR abs/2210.10452 (2022) - [i17]Royson Lee, Rui Li, Stylianos I. Venieris, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation. CoRR abs/2212.07886 (2022) - 2021
- [c13]Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton:
Efficient Wasserstein Natural Gradients for Reinforcement Learning. ICLR 2021 - [i16]Ferenc Huszár, Sofia Ira Ktena, Conor O'Brien, Luca Belli, Andrew Schlaikjer, Moritz Hardt:
Algorithmic Amplification of Politics on Twitter. CoRR abs/2110.11010 (2021) - [i15]Anna Kerekes, Anna Mészáros, Ferenc Huszár:
Depth Without the Magic: Inductive Bias of Natural Gradient Descent. CoRR abs/2111.11542 (2021) - 2020
- [c12]Dalin Guo, Sofia Ira Ktena, Pranay Kumar Myana, Ferenc Huszar, Wenzhe Shi, Alykhan Tejani, Michael Kneier, Sourav Das:
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations. RecSys 2020: 456-461 - [c11]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, Wenzhe Shi:
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. RecSys 2020: 521-526 - [i14]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, Wenzhe Shi:
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. CoRR abs/2007.14523 (2020) - [i13]Dalin Guo, Sofia Ira Ktena, Ferenc Huszar, Pranay Kumar Myana, Wenzhe Shi, Alykhan Tejani:
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations. CoRR abs/2008.00727 (2020) - [i12]Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton:
Efficient Wasserstein Natural Gradients for Reinforcement Learning. CoRR abs/2010.05380 (2020)
2010 – 2019
- 2019
- [c10]Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszár, Steven Yoo, Wenzhe Shi:
Addressing delayed feedback for continuous training with neural networks in CTR prediction. RecSys 2019: 187-195 - [i11]Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszar, Steven Yoo, Wenzhe Shi:
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction. CoRR abs/1907.06558 (2019) - 2018
- [c9]Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre:
BRUNO: A Deep Recurrent Model for Exchangeable Data. NeurIPS 2018: 7190-7198 - [c8]Katherine Storrs, Sebastiaan Van Leuven, Steve Kojder, Lucas Theis, Ferenc Huszar:
Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices. PCS 2018: 169-173 - [i10]Lucas Theis, Iryna Korshunova, Alykhan Tejani, Ferenc Huszár:
Faster gaze prediction with dense networks and Fisher pruning. CoRR abs/1801.05787 (2018) - 2017
- [c7]Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi:
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. CVPR 2017: 105-114 - [c6]Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár:
Amortised MAP Inference for Image Super-resolution. ICLR 2017 - [c5]Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár:
Lossy Image Compression with Compressive Autoencoders. ICLR (Poster) 2017 - [i9]Ferenc Huszár:
Variational Inference using Implicit Distributions. CoRR abs/1702.08235 (2017) - [i8]Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár:
Lossy Image Compression with Compressive Autoencoders. CoRR abs/1703.00395 (2017) - [i7]Ferenc Huszár:
On Quadratic Penalties in Elastic Weight Consolidation. CoRR abs/1712.03847 (2017) - 2016
- [c4]Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang:
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. CVPR 2016: 1874-1883 - [i6]Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi:
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. CoRR abs/1609.04802 (2016) - [i5]Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang:
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. CoRR abs/1609.05158 (2016) - [i4]Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew P. Aitken, Christian Ledig, Zehan Wang:
Is the deconvolution layer the same as a convolutional layer? CoRR abs/1609.07009 (2016) - [i3]Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár:
Amortised MAP Inference for Image Super-resolution. CoRR abs/1610.04490 (2016) - 2015
- [i2]Ferenc Huszar:
How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? CoRR abs/1511.05101 (2015) - 2012
- [c3]Neil Houlsby, José Miguel Hernández-Lobato, Ferenc Huszar, Zoubin Ghahramani:
Collaborative Gaussian Processes for Preference Learning. NIPS 2012: 2105-2113 - [c2]Ferenc Huszar, David Duvenaud:
Optimally-Weighted Herding is Bayesian Quadrature. UAI 2012: 377-386 - 2011
- [c1]Simon Lacoste-Julien, Ferenc Huszar, Zoubin Ghahramani:
Approximate inference for the loss-calibrated Bayesian. AISTATS 2011: 416-424 - [i1]Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Máté Lengyel:
Bayesian Active Learning for Classification and Preference Learning. CoRR abs/1112.5745 (2011)
Coauthor Index
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last updated on 2024-09-04 01:19 CEST by the dblp team
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