Geoffrey Roeder

According to our database, Geoffrey Roeder authored at least 10 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites.

Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz

Proceedings of the 8th ACM Symposium on Computational Fabrication, 2023

2022

Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry.

Faris M. Sbahi, Antonio J. Martinez, Sahil Patel, Dmitri Saberi, Jae Hyeon Yoo, Geoffrey Roeder, Guillaume Verdon

CoRR, 2022

2021

Probabilistic Graphical Models and Tensor Networks: A Hybrid Framework.

Jacob Miller, Geoffrey Roeder, Tai-Danae Bradley

CoRR, 2021

On Linear Identifiability of Learned Representations.

Geoffrey Roeder, Luke Metz, Durk Kingma

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

On Linear Identifiability of Learned Representations.

Geoffrey Roeder, Luke Metz, Diederik P. Kingma

CoRR, 2020

Learning Composable Energy Surrogates for PDE Order Reduction.

Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems.

Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation.

Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference.

Geoffrey Roeder, Yuhuai Wu, David Duvenaud

CoRR, 2017

Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference.

Geoffrey Roeder, Yuhuai Wu, David Duvenaud

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017