Geoffrey Roeder

15 papers A* 3Journal 9Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Local Learning Rules for Out-of-Equilibrium Physical Generative Models. CoRR Cyrill Bösch, Geoffrey Roeder, Marc Serra-Garcia, Ryan P. Adams
2023	—	`conf`	More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. SCF Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz
2022	J	`jnl`	More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. CoRR Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz
2022	J	`jnl`	Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry. CoRR Faris M. Sbahi, Antonio J. Martinez, Sahil Patel, Dmitri Saberi, Jae Hyeon Yoo, Geoffrey Roeder, Guillaume Verdon
2021	A*	`conf`	On Linear Identifiability of Learned Representations. ICML Geoffrey Roeder, Luke Metz, Durk Kingma
2021	J	`jnl`	Probabilistic Graphical Models and Tensor Networks: A Hybrid Framework. CoRR Jacob Miller, Geoffrey Roeder, Tai-Danae Bradley
2020	A*	`conf`	Learning Composable Energy Surrogates for PDE Order Reduction. NeurIPS Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
2020	J	`jnl`	Learning Composable Energy Surrogates for PDE Order Reduction. CoRR Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
2020	J	`jnl`	On Linear Identifiability of Learned Representations. CoRR Geoffrey Roeder, Luke Metz, Diederik P. Kingma
2019	A*	`conf`	Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. ICML Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds
2019	J	`jnl`	Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. CoRR Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds
2018	—	`conf`	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. ICLR (Poster) Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
2017	J	`jnl`	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
2017	J	`jnl`	Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. CoRR Geoffrey Roeder, Yuhuai Wu, David Duvenaud
2017	—	`conf`	Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. NIPS Geoffrey Roeder, Yuhuai Wu, David Duvenaud