Roger B. Grosse

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 110

Top Venues

CoRR

NeurIPS

ICML

ICLR (Poster)

Publications

Nathan Ng, Roger B. Grosse, Marzyeh Ghassemi
Measuring Stochastic Data Complexity with Boltzmann Influence Functions. CoRR (2024)
Jin Peng Zhou, Yuhuai Wu, Qiyang Li, Roger B. Grosse
REFACTOR: Learning to Extract Theorems from Proofs. CoRR (2024)
Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff G. Schneider, Eduard H. Hovy, Roger B. Grosse, Eric P. Xing
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. CoRR (2024)
Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger B. Grosse
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo. CoRR (2024)
Nikita Dhawan, Sicong Huang, Juhan Bae, Roger B. Grosse
Efficient Parametric Approximations of Neural Network Function Space Distance. CoRR (2023)
Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger B. Grosse, Alireza Makhzani
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. CoRR (2023)
Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster
Similarity-based cooperative equilibrium. NeurIPS (2023)
Roger B. Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamile Lukosiute, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, Samuel R. Bowman
Studying Large Language Model Generalization with Influence Functions. CoRR (2023)
Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse
If Influence Functions are the Answer, Then What is the Question? NeurIPS (2022)
Stephen Zhao, Chris Lu, Roger B. Grosse, Jakob N. Foerster
Proximal Learning With Opponent-Learning Awareness. NeurIPS (2022)
Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster
Similarity-based Cooperation. CoRR (2022)
Paul Vicol, Jonathan Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
On Implicit Bias in Overparameterized Bilevel Optimization. CoRR (2022)
Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
On Implicit Bias in Overparameterized Bilevel Optimization. ICML (2022)
Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse
Amortized Proximal Optimization. NeurIPS (2022)
Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse
Amortized Proximal Optimization. CoRR (2022)
Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse
If Influence Functions are the Answer, Then What is the Question? CoRR (2022)
Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, Zico Kolter, Roger B. Grosse
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. CoRR (2022)
Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger B. Grosse
Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve. CoRR (2022)
Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger B. Grosse
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. NeurIPS (2022)
Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. AISTATS (2022)
Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger B. Grosse
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints. J. Mach. Learn. Res. 22 (2021)
Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse
Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. CoRR (2021)
Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. NeurIPS (2021)
Chaoqi Wang, Shengyang Sun, Roger B. Grosse
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? AISTATS (2021)
Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, Christian Szegedy
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. CoRR (2021)
Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. ICML (2021)
James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes. CoRR (2021)
James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
On Monotonic Linear Interpolation of Neural Network Parameters. ICML (2021)
Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks. AISTATS (2021)
Cem Anil, Guodong Zhang, Yuhuai Wu, Roger B. Grosse
Learning to Give Checkable Answers with Prover-Verifier Games. CoRR (2021)
Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. CoRR (2021)
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Sanjit A. Seshia, Fahiem Bacchus
Learning Branching Heuristics for Propositional Model Counting. AAAI (2021)
Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. CoRR (2021)
Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, Christian Szegedy
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. ICML (2021)
Juhan Bae, Roger B. Grosse
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians. NeurIPS (2020)
Juhan Bae, Roger B. Grosse
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians. CoRR (2020)
Shun-ichi Amari, Jimmy Ba, Roger B. Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
When Does Preconditioning Help or Hurt Generalization? CoRR (2020)
Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse
Regularized linear autoencoders recover the principal components, eventually. CoRR (2020)
Yuhuai Wu, Honghua Dong, Roger B. Grosse, Jimmy Ba
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning. CoRR (2020)
Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse
Regularized linear autoencoders recover the principal components, eventually. NeurIPS (2020)
Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen
Understanding and mitigating exploding inverses in invertible neural networks. CoRR (2020)
Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse
Evaluating Lossy Compression Rates of Deep Generative Models. ICML (2020)
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Edward A. Lee, Sanjit A. Seshia, Fahiem Bacchus
Learning Branching Heuristics for Propositional Model Counting. CoRR (2020)
Yuhuai Wu, Albert Jiang, Jimmy Ba, Roger B. Grosse
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving. CoRR (2020)
Chaoqi Wang, Shengyang Sun, Roger B. Grosse
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? CoRR (2020)
Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger B. Grosse
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints. CoRR (2020)
Chaoqi Wang, Guodong Zhang, Roger B. Grosse
Picking Winning Tickets Before Training by Preserving Gradient Flow. ICLR (2020)
Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse
Evaluating Lossy Compression Rates of Deep Generative Models. CoRR (2020)
Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. CoRR (2019)
Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. NeurIPS (2019)
Guodong Zhang, James Martens, Roger B. Grosse
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks. NeurIPS (2019)
Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger B. Grosse
Functional variational Bayesian Neural Networks. ICLR (Poster) (2019)
Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse
Three Mechanisms of Weight Decay Regularization. ICLR (Poster) (2019)
James Lucas, Shengyang Sun, Richard S. Zemel, Roger B. Grosse
Aggregated Momentum: Stability Through Passive Damping. ICLR (Poster) (2019)
James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. NeurIPS (2019)
Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. CoRR (2019)
Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. CoRR (2019)
Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. ICLR (Poster) (2019)
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. NeurIPS (2019)
Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer. ICLR (Poster) (2019)
James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
Understanding Posterior Collapse in Generative Latent Variable Models. DGS@ICLR (2019)
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. CoRR (2019)
Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. ICML (2019)
Cem Anil, James Lucas, Roger B. Grosse
Sorting Out Lipschitz Function Approximation. ICML (2019)
James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. CoRR (2019)
Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger B. Grosse
Functional Variational Bayesian Neural Networks. CoRR (2019)
Guodong Zhang, James Martens, Roger B. Grosse
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks. CoRR (2019)
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel
Adversarial Distillation of Bayesian Neural Network Posteriors. ICML (2018)
Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer. CoRR (2018)
Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse
Noisy Natural Gradient as Variational Inference. ICML (2018)
Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
Isolating Sources of Disentanglement in Variational Autoencoders. CoRR (2018)
Juhan Bae, Guodong Zhang, Roger B. Grosse
Eigenvalue Corrected Noisy Natural Gradient. CoRR (2018)
Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. ICLR (Poster) (2018)
Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse
Reversible Recurrent Neural Networks. NeurIPS (2018)
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. CoRR (2018)
Kevin Luk, Roger B. Grosse
A Coordinate-Free Construction of Scalable Natural Gradient. CoRR (2018)
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. ICLR (Poster) (2018)
Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
Isolating Sources of Disentanglement in Variational Autoencoders. ICLR (Workshop) (2018)
Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse
Three Mechanisms of Weight Decay Regularization. CoRR (2018)
Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. CoRR (2018)
Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse
Reversible Recurrent Neural Networks. CoRR (2018)
Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse
Differentiable Compositional Kernel Learning for Gaussian Processes. ICML (2018)
Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens
Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. ICLR (Workshop) (2018)
Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse
Differentiable Compositional Kernel Learning for Gaussian Processes. CoRR (2018)
Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
Isolating Sources of Disentanglement in Variational Autoencoders. NeurIPS (2018)
James Lucas, Richard S. Zemel, Roger B. Grosse
Aggregated Momentum: Stability Through Passive Damping. CoRR (2018)
Cem Anil, James Lucas, Roger B. Grosse
Sorting out Lipschitz function approximation. CoRR (2018)
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel
Adversarial Distillation of Bayesian Neural Network Posteriors. CoRR (2018)
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse
On the Quantitative Analysis of Decoder-Based Generative Models. ICLR (Poster) (2017)
Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse
Noisy Natural Gradient as Variational Inference. CoRR (2017)
Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse
Discovering and Exploiting Additive Structure for Bayesian Optimization. AISTATS (2017)
Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse
The Reversible Residual Network: Backpropagation Without Storing Activations. NIPS (2017)
Jimmy Ba, Roger B. Grosse, James Martens
Distributed Second-Order Optimization using Kronecker-Factored Approximations. ICLR (Poster) (2017)
Yuhuai Wu, Elman Mansimov, Roger B. Grosse, Shun Liao, Jimmy Ba
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation NIPS (2017)
Yuhuai Wu, Elman Mansimov, Shun Liao, Roger B. Grosse, Jimmy Ba
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. CoRR (2017)
Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse
The Reversible Residual Network: Backpropagation Without Storing Activations. CoRR (2017)
Roger B. Grosse, James Martens
A Kronecker-factored approximate Fisher matrix for convolution layers. CoRR (2016)
Roger B. Grosse, James Martens
A Kronecker-factored approximate Fisher matrix for convolution layers. ICML (2016)
Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
Measuring the reliability of MCMC inference with bidirectional Monte Carlo. CoRR (2016)
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse
On the Quantitative Analysis of Decoder-Based Generative Models. CoRR (2016)

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Publication Activity (10 Years)

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Roger B. Grosse