Search Results for author: Ferenc Huszar

Found 10 papers, 4 papers with code

Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics

no code implementations3 Feb 2022 Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszar, Rumman Chowdhury

We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.

Efficient Wasserstein Natural Gradients for Reinforcement Learning

1 code implementation ICLR 2021 Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton

A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL).

Policy Gradient Methods reinforcement-learning

Deep Bayesian Bandits: Exploring in Online Personalized Recommendations

no code implementations3 Aug 2020 Dalin Guo, Sofia Ira Ktena, Ferenc Huszar, Pranay Kumar Myana, Wenzhe Shi, Alykhan Tejani

Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias.

Recommendation Systems

Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction

no code implementations15 Jul 2019 Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszar, Steven Yoo, Wenzhe Shi

The focus of this paper is to identify the best combination of loss functions and models that enable large-scale learning from a continuous stream of data in the presence of delayed labels.

Click-Through Rate Prediction

Is the deconvolution layer the same as a convolutional layer?

5 code implementations22 Sep 2016 Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew Aitken, Christian Ledig, Zehan Wang

In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented.

Optimally-Weighted Herding is Bayesian Quadrature

no code implementations9 Aug 2014 Ferenc Huszar, David Duvenaud

We show that the criterion minimised when selecting samples in kernel herding is equivalent to the posterior variance in Bayesian quadrature.

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