Roger B. Grosse

According to our database, Roger B. Grosse authored at least 84 papers between 2007 and 2024.

Collaborative distances:

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

2024

Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data.

Johannes Treutlein, Dami Choi, Jan Betley, Cem Anil, Samuel Marks, Roger Baker Grosse, Owain Evans

CoRR, 2024

What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions.

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

CoRR, 2024

Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo.

Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Baker Grosse

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Measuring Stochastic Data Complexity with Boltzmann Influence Functions.

Nathan H. Ng, Roger Baker Grosse, Marzyeh Ghassemi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

REFACTOR: Learning to Extract Theorems from Proofs.

Jin Peng Zhou, Yuhuai Wu, Qiyang Li, Roger Baker Grosse

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Studying Large Language Model Generalization with Influence Functions.

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

CoRR, 2023

Similarity-based cooperative equilibrium.

Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Parametric Approximations of Neural Network Function Space Distance.

Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Baker Grosse

Proceedings of the International Conference on Machine Learning, 2023

Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve.

Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger Baker Grosse

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Similarity-based Cooperation.

Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster

CoRR, 2022

Proximal Learning With Opponent-Learning Awareness.

Stephen Zhao, Chris Lu, Roger B. Grosse, Jakob N. Foerster

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Amortized Proximal Optimization.

Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

If Influence Functions are the Answer, Then What is the Question?

Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Path Independent Equilibrium Models Can Better Exploit Test-Time Computation.

Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Implicit Bias in Overparameterized Bilevel Optimization.

Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse

Proceedings of the International Conference on Machine Learning, 2022

Improving Mutual Information Estimation with Annealed and Energy-Based Bounds.

Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger Baker Grosse, Alireza Makhzani

Proceedings of the Tenth International Conference on Learning Representations, 2022

Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization.

Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints.

Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger B. Grosse

J. Mach. Learn. Res., 2021

Learning to Give Checkable Answers with Prover-Verifier Games.

Cem Anil, Guodong Zhang, Yuhuai Wu, Roger B. Grosse

CoRR, 2021

Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes.

James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse

CoRR, 2021

Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization.

Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse

CoRR, 2021

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise.

Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning.

Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, Christian Szegedy

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

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition.

Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse

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

On Monotonic Linear Interpolation of Neural Network Parameters.

James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse

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

INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving.

Yuhuai Wu, Albert Q. Jiang, Jimmy Ba, Roger Baker Grosse

Proceedings of the 9th International Conference on Learning Representations, 2021

When does preconditioning help or hurt generalization?

Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu

Proceedings of the 9th International Conference on Learning Representations, 2021

Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?

Chaoqi Wang, Shengyang Sun, Roger B. Grosse

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Understanding and Mitigating Exploding Inverses in Invertible Neural Networks.

Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Learning Branching Heuristics for Propositional Model Counting.

Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Sanjit A. Seshia, Fahiem Bacchus

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning.

Yuhuai Wu, Honghua Dong, Roger B. Grosse, Jimmy Ba

CoRR, 2020

Learning Branching Heuristics for Propositional Model Counting.

Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Edward A. Lee, Sanjit A. Seshia, Fahiem Bacchus

CoRR, 2020

Regularized linear autoencoders recover the principal components, eventually.

Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse

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

Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians.

Juhan Bae, Roger B. Grosse

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

Evaluating Lossy Compression Rates of Deep Generative Models.

Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse

Proceedings of the 37th International Conference on Machine Learning, 2020

Picking Winning Tickets Before Training by Preserving Gradient Flow.

Chaoqi Wang, Guodong Zhang, Roger B. Grosse

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks.

Guodong Zhang, James Martens, Roger B. Grosse

CoRR, 2019

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks.

Guodong Zhang, James Martens, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model.

Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse.

James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks.

Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis.

Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang

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

Sorting Out Lipschitz Function Approximation.

Cem Anil, James Lucas, Roger B. Grosse

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

Three Mechanisms of Weight Decay Regularization.

Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse

Proceedings of the 7th International Conference on Learning Representations, 2019

Functional variational Bayesian Neural Networks.

Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger B. Grosse

Proceedings of the 7th International Conference on Learning Representations, 2019

Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions.

Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse

Proceedings of the 7th International Conference on Learning Representations, 2019

Understanding Posterior Collapse in Generative Latent Variable Models.

James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Aggregated Momentum: Stability Through Passive Damping.

James Lucas, Shengyang Sun, Richard S. Zemel, Roger B. Grosse

Proceedings of the 7th International Conference on Learning Representations, 2019

TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer.

Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Eigenvalue Corrected Noisy Natural Gradient.

Juhan Bae, Guodong Zhang, Roger B. Grosse

CoRR, 2018

A Coordinate-Free Construction of Scalable Natural Gradient.

Kevin Luk, Roger B. Grosse

CoRR, 2018

Aggregated Momentum: Stability Through Passive Damping.

James Lucas, Richard S. Zemel, Roger B. Grosse

CoRR, 2018

Reversible Recurrent Neural Networks.

Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Isolating Sources of Disentanglement in Variational Autoencoders.

Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Noisy Natural Gradient as Variational Inference.

Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse

Proceedings of the 35th International Conference on Machine Learning, 2018

Adversarial Distillation of Bayesian Neural Network Posteriors.

Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel

Proceedings of the 35th International Conference on Machine Learning, 2018

Differentiable Compositional Kernel Learning for Gaussian Processes.

Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse

Proceedings of the 35th International Conference on Machine Learning, 2018

Understanding Short-Horizon Bias in Stochastic Meta-Optimization.

Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse

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

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches.

Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse

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

Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure.

Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens

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

2017

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

Yuhuai Wu, Elman Mansimov, Roger B. Grosse, Shun Liao, Jimmy Ba

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

The Reversible Residual Network: Backpropagation Without Storing Activations.

Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse

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

On the Quantitative Analysis of Decoder-Based Generative Models.

Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse

Proceedings of the 5th International Conference on Learning Representations, 2017

Distributed Second-Order Optimization using Kronecker-Factored Approximations.

Jimmy Ba, Roger B. Grosse, James Martens

Proceedings of the 5th International Conference on Learning Representations, 2017

Discovering and Exploiting Additive Structure for Bayesian Optimization.

Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Importance Weighted Autoencoders.

Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov

Proceedings of the 4th International Conference on Learning Representations, 2016

Measuring the reliability of MCMC inference with bidirectional Monte Carlo.

Roger B. Grosse, Siddharth Ancha, Daniel M. Roy

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Kronecker-factored approximate Fisher matrix for convolution layers.

Roger B. Grosse, James Martens

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Sandwiching the marginal likelihood using bidirectional Monte Carlo.

Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams

CoRR, 2015

Statistical Inference, Learning and Models in Big Data.

Beate Franke, Jean-François Plante, Ribana Roscher, Annie Lee, Cathal Smyth, Armin Hatefi, Fuqi Chen, Einat Gil, Alexander G. Schwing, Alessandro Selvitella, Michael M. Hoffman, Roger B. Grosse, Dieter Hendricks, Nancy Reid

CoRR, 2015

Learning Wake-Sleep Recurrent Attention Models.

Jimmy Ba, Ruslan Salakhutdinov, Roger B. Grosse, Brendan J. Frey

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Optimizing Neural Networks with Kronecker-factored Approximate Curvature.

James Martens, Roger B. Grosse

Proceedings of the 32nd International Conference on Machine Learning, 2015

Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix.

Roger B. Grosse, Ruslan Salakhutdinov

Proceedings of the 32nd International Conference on Machine Learning, 2015

Accurate and conservative estimates of MRF log-likelihood using reverse annealing.

Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Model selection in compositional spaces.

Roger Baker Grosse

PhD thesis, 2014

Testing MCMC code.

Roger B. Grosse, David Kristjanson Duvenaud

CoRR, 2014

Automatic Construction and Natural-Language Description of Nonparametric Regression Models.

James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Annealing between distributions by averaging moments.

Roger B. Grosse, Chris J. Maddison, Ruslan Salakhutdinov

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Structure Discovery in Nonparametric Regression through Compositional Kernel Search.

David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Exploiting compositionality to explore a large space of model structures.

Roger B. Grosse, Ruslan Salakhutdinov, William T. Freeman, Joshua B. Tenenbaum

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011

Unsupervised learning of hierarchical representations with convolutional deep belief networks.

Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng

Commun. ACM, 2011

2009

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.

Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Ground truth dataset and baseline evaluations for intrinsic image algorithms.

Roger B. Grosse, Micah K. Johnson, Edward H. Adelson, William T. Freeman

Proceedings of the IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, September 27, 2009

2007

Shift-Invariance Sparse Coding for Audio Classification.

Roger B. Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng

Proceedings of the UAI 2007, 2007