Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. - Geoffrey Roeder; Yuhuai Wu; David Duvenaud (NIPS, 2017)

Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. - Geoffrey Roeder; Yuhuai Wu; David Duvenaud (CoRR, 2017)

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. - Will Grathwohl; Dami Choi; Yuhuai Wu; Geoffrey Roeder; David Duvenaud (ICLR (Poster), 2018)

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. - Geoffrey Roeder; Paul K. Grant; Andrew Phillips; Neil Dalchau; Edward Meeds (ICML, 2019)

Learning Composable Energy Surrogates for PDE Order Reduction. - Alex Beatson; Jordan T. Ash; Geoffrey Roeder; Tianju Xue; Ryan P. Adams (NeurIPS, 2020)

On Linear Identifiability of Learned Representations. - Geoffrey Roeder; Luke Metz; Diederik P. Kingma (CoRR, 2020)

On Linear Identifiability of Learned Representations. - Geoffrey Roeder; Luke Metz; Durk Kingma (ICML, 2021)

Probabilistic Graphical Models and Tensor Networks: A Hybrid Framework. - Jacob Miller 003; Geoffrey Roeder; Tai-Danae Bradley (CoRR, 2021)

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)

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 (SCF, 2023)

Local Learning Rules for Out-of-Equilibrium Physical Generative Models. - Cyrill Bösch; Geoffrey Roeder; Marc Serra-Garcia; Ryan P. Adams (CoRR, 2025)