Grosse, Roger

23 publications

NeurIPS 2024 Connecting the Dots: LLMs Can Infer and Verbalize Latent Structure from Disparate Training Data Johannes Treutlein, Dami Choi, Jan Betley, Sam Marks, Cem Anil, Roger Grosse, Owain Evans

NeurIPS 2024 Training Data Attribution via Approximate Unrolling Juhan Bae, Wu Lin, Jonathan Lorraine, Roger Grosse

AISTATS 2021 Beyond Marginal Uncertainty: How Accurately Can Bayesian Regression Models Estimate Posterior Predictive Correlations? Chaoqi Wang, Shengyang Sun, Roger Grosse

AISTATS 2021 Understanding and Mitigating Exploding Inverses in Invertible Neural Networks Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, Joern-Henrik Jacobsen

JMLR 2021 A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger Grosse

ICML 2020 Evaluating Lossy Compression Rates of Deep Generative Models Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse

ICLR 2020 Picking Winning Tickets Before Training by Preserving Gradient Flow Chaoqi Wang, Guodong Zhang, Roger Grosse

ICLR 2019 Aggregated Momentum: Stability Through Passive Damping James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse

ICML 2019 EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis Chaoqi Wang, Roger Grosse, Sanja Fidler, Guodong Zhang

ICLR 2019 Functional Variational Bayesian Neural Networks Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger Grosse

ICLR 2019 Self-Tuning Networks: Bilevel Optimization of Hyperparameters Using Structured Best-Response Functions Matthew Mackay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger Grosse

ICML 2019 Sorting Out Lipschitz Function Approximation Cem Anil, James Lucas, Roger Grosse

ICLR 2019 Three Mechanisms of Weight Decay Regularization Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger Grosse

ICLRW 2019 Understanding Posterior Collapse in Generative Latent Variable Models James Lucas, George Tucker, Roger Grosse, Mohammad Norouzi

ICML 2018 Adversarial Distillation of Bayesian Neural Network Posteriors Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel

ICML 2018 Differentiable Compositional Kernel Learning for Gaussian Processes Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger Grosse

ICLR 2018 Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

ICML 2018 Noisy Natural Gradient as Variational Inference Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse

ICLR 2018 Understanding Short-Horizon Bias in Stochastic Meta-Optimization Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse.

ICML 2016 A Kronecker-Factored Approximate Fisher Matrix for Convolution Layers Roger Grosse, James Martens

ICML 2015 Optimizing Neural Networks with Kronecker-Factored Approximate Curvature James Martens, Roger Grosse

ICML 2015 Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix Roger Grosse, Ruslan Salakhudinov

ICML 2013 Structure Discovery in Nonparametric Regression Through Compositional Kernel Search David Duvenaud, James Lloyd, Roger Grosse, Joshua Tenenbaum, Ghahramani Zoubin