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Your Policy Regularizer is Secretly an Adversary



Rob Brekelmans , Tim Genewein , Jordi Grau-Moya , Grégoire Delétang , Markus Kunesch , Shane Legg , Pedro Ortega

* 10 pages main text; added worked example 

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Model-Free Risk-Sensitive Reinforcement Learning



Grégoire Delétang , Jordi Grau-Moya , Markus Kunesch , Tim Genewein , Rob Brekelmans , Shane Legg , Pedro A. Ortega

* DeepMind Tech Report: 13 pages, 4 figures 

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q-Paths: Generalizing the Geometric Annealing Path using Power Means



Vaden Masrani , Rob Brekelmans , Thang Bui , Frank Nielsen , Aram Galstyan , Greg Ver Steeg , Frank Wood

* arXiv admin note: text overlap with arXiv:2012.07823 

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Likelihood Ratio Exponential Families



Rob Brekelmans , Frank Nielsen , Alireza Makhzani , Aram Galstyan , Greg Ver Steeg

* NeurIPS Workshop on Deep Learning through Information Geometry 

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Annealed Importance Sampling with q-Paths



Rob Brekelmans , Vaden Masrani , Thang Bui , Frank Wood , Aram Galstyan , Greg Ver Steeg , Frank Nielsen

* NeurIPS Workshop on Deep Learning through Information Geometry (Best Paper Award) 

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Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective



Vu Nguyen , Vaden Masrani , Rob Brekelmans , Michael A. Osborne , Frank Wood

* NeurIPS 2020 

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Gaussian Process Bandit Optimization of theThermodynamic Variational Objective



Vu Nguyen , Vaden Masrani , Rob Brekelmans , Michael A. Osborne , Frank Wood

* NeurIPS 2020 

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All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference



Rob Brekelmans , Vaden Masrani , Frank Wood , Greg Ver Steeg , Aram Galstyan

* ICML 2020 

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Discovery and Separation of Features for Invariant Representation Learning



Ayush Jaiswal , Rob Brekelmans , Daniel Moyer , Greg Ver Steeg , Wael AbdAlmageed , Premkumar Natarajan

* 10 pages, 3 figures 

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