1k citations · 8k across the 112 of their papers we have counts for
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Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg +4
The instability of Generative Adversarial Network (GAN) training has frequently been attributed to gradient descent. Consequently, recent methods have aimed to tailor the models an…
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Sven Gowal, Chongli Qin, Jonathan Uesato +2
Adversarial training and its variants have become de facto standards for learning robust deep neural networks. In this paper, we explore the landscape around adversarial training i…
Adversarial Robustness through Local Linearization
Chongli Qin, James Martens, Sven Gowal +6
Adversarial training is an effective methodology for training deep neural networks that are robust against adversarial, norm-bounded perturbations. However, the computational cost…
Degenerate Feedback Loops in Recommender Systems
Ray Jiang, Silvia Chiappa, Tor Lattimore +2
Machine learning is used extensively in recommender systems deployed in products. The decisions made by these systems can influence user beliefs and preferences which in turn affec…
Meta-Learning surrogate models for sequential decision making
Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim +5
We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.…
CompILE: Compositional Imitation Learning and Execution
Thomas Kipf, Yujia Li, Hanjun Dai +5
We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demons…
Batched Large-scale Bayesian Optimization in High-dimensional Spaces
Zi Wang, Clement Gehring, Pushmeet Kohli +1
Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved wit…
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent +5
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the dat…
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Zi Wang, Chengtao Li, Stefanie Jegelka +1
Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box fun…
Inducing Interpretable Representations with Variational Autoencoders
N. Siddharth, Brooks Paige, Alban Desmaison +5
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representation…
Learning an Interactive Segmentation System
Hannes Nickisch, Pushmeet Kohli, Carsten Rother
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user.…