Roger B. Grosse

141 papers A* 36A 7Journal 79Unranked 19

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Gauss-Newton Unlearning for the LLM Era. CoRR Lev McKinney, Anvith Thudi, Juhan Bae, Tara Rezaei, Nicolas Papernot, Sheila A. McIlraith, Roger B. Grosse
2025	J	`jnl`	Better Training Data Attribution via Better Inverse Hessian-Vector Products. CoRR Andrew Wang, Elisa Nguyen, Runshi Yang, Juhan Bae, Sheila A. McIlraith, Roger B. Grosse
2025	J	`jnl`	Distributional Training Data Attribution. CoRR Bruno Mlodozeniec, Isaac Reid, Sam Power, David Krueger, Murat A. Erdogdu, Richard E. Turner, Roger B. Grosse
2025	J	`jnl`	Forecasting Rare Language Model Behaviors. CoRR Erik Jones, Meg Tong, Jesse Mu, Mohammed Mahfoud, Jan Leike, Roger B. Grosse, Jared Kaplan, William Fithian, Ethan Perez, Mrinank Sharma
2025	J	`jnl`	Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference. CoRR Stephen Zhao, Aidan Li, Rob Brekelmans, Roger B. Grosse
2025	J	`jnl`	Spectral-factorized Positive-definite Curvature Learning for NN Training. CoRR Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Roger B. Grosse
2025	J	`jnl`	Through a Steerable Lens: Magnifying Neural Network Interpretability via Phase-Based Extrapolation. CoRR Farzaneh Mahdisoltani, Saeed Mahdisoltani, Roger B. Grosse, David J. Fleet
2025	J	`jnl`	Understanding and Improving Shampoo and SOAP via Kullback-Leibler Minimization. CoRR Wu Lin, Scott C. Lowe, Felix Dangel, Runa Eschenhagen, Zikun Xu, Roger B. Grosse
2024	A*	`conf`	Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. NeurIPS Johannes Treutlein, Dami Choi, Jan Betley, Samuel Marks, Cem Anil, Roger B. Grosse, Owain Evans
2024	A*	`conf`	Many-shot Jailbreaking. NeurIPS Cem Anil, Esin Durmus, Nina Panickssery, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Meg Tong, Jesse Mu, Daniel Ford, Francesco Mosconi, Rajashree Agrawal, Rylan Schaeffer, Naomi Bashkansky, Samuel Svenningsen, Mike Lambert, Ansh Radhakrishnan, Carson Denison, Evan Hubinger, Yuntao Bai, Trenton Bricken, Timothy Maxwell, Nicholas Schiefer, James Sully, Alex Tamkin, Tamera Lanham, Karina Nguyen, Tomek Korbak, Jared Kaplan, Deep Ganguli, Samuel R. Bowman, Ethan Perez, Roger B. Grosse, David Kristjanson Duvenaud
2024	J	`jnl`	Measuring Stochastic Data Complexity with Boltzmann Influence Functions. CoRR Nathan Ng, Roger B. Grosse, Marzyeh Ghassemi
2024	J	`jnl`	Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo. CoRR Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger B. Grosse
2024	J	`jnl`	REFACTOR: Learning to Extract Theorems from Proofs. CoRR Jin Peng Zhou, Yuhuai Wu, Qiyang Li, Roger B. Grosse
2024	J	`jnl`	Sabotage Evaluations for Frontier Models. CoRR Joe Benton, Misha Wagner, Eric Christiansen, Cem Anil, Ethan Perez, Jai Srivastav, Esin Durmus, Deep Ganguli, Shauna Kravec, Buck Shlegeris, Jared Kaplan, Holden Karnofsky, Evan Hubinger, Roger B. Grosse, Samuel R. Bowman, David Duvenaud
2024	J	`jnl`	Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. CoRR Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam S. Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger B. Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul F. Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez
2024	J	`jnl`	Training Data Attribution via Approximate Unrolled Differentiation. CoRR Juhan Bae, Wu Lin, Jonathan Lorraine, Roger B. Grosse
2024	A*	`conf`	Training Data Attribution via Approximate Unrolling. NeurIPS Juhan Bae, Wu Lin, Jonathan Lorraine, Roger B. Grosse
2024	J	`jnl`	What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. CoRR 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
2023	—	`conf`	Discovering Language Model Behaviors with Model-Written Evaluations. ACL (Findings) Ethan Perez, Sam Ringer, Kamile Lukosiute, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Benjamin Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemí Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger B. Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, Jared Kaplan
2023	J	`jnl`	Efficient Parametric Approximations of Neural Network Function Space Distance. CoRR Nikita Dhawan, Sicong Huang, Juhan Bae, Roger B. Grosse
2023	J	`jnl`	Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. CoRR Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger B. Grosse, Alireza Makhzani
2023	A*	`conf`	Similarity-based cooperative equilibrium. NeurIPS Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster
2023	J	`jnl`	Studying Large Language Model Generalization with Influence Functions. CoRR 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
2022	A*	`conf`	Amortized Proximal Optimization. NeurIPS Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse
2022	J	`jnl`	Amortized Proximal Optimization. CoRR Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse
2022	J	`jnl`	Discovering Language Model Behaviors with Model-Written Evaluations. CoRR Ethan Perez, Sam Ringer, Kamile Lukosiute, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Ben Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemí Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger B. Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, Jared Kaplan
2022	A*	`conf`	If Influence Functions are the Answer, Then What is the Question? NeurIPS Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse
2022	J	`jnl`	If Influence Functions are the Answer, Then What is the Question? CoRR Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse
2022	J	`jnl`	Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve. CoRR Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger B. Grosse
2022	A	`conf`	Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. AISTATS Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse
2022	A*	`conf`	On Implicit Bias in Overparameterized Bilevel Optimization. ICML Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
2022	J	`jnl`	On Implicit Bias in Overparameterized Bilevel Optimization. CoRR Paul Vicol, Jonathan Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
2022	A*	`conf`	Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. NeurIPS Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger B. Grosse
2022	J	`jnl`	Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. CoRR Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, Zico Kolter, Roger B. Grosse
2022	A*	`conf`	Proximal Learning With Opponent-Learning Awareness. NeurIPS Stephen Zhao, Chris Lu, Roger B. Grosse, Jakob N. Foerster
2022	J	`jnl`	Similarity-based Cooperation. CoRR Caspar Oesterheld, Johannes Treutlein, Roger B. Grosse, Vincent Conitzer, Jakob N. Foerster
2022	J	`jnl`	Toy Models of Superposition. CoRR Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, Roger B. Grosse, Sam McCandlish, Jared Kaplan, Dario Amodei, Martin Wattenberg, Christopher Olah
2021	J	`jnl`	A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints. J. Mach. Learn. Res. Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger B. Grosse
2021	J	`jnl`	Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes. CoRR James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
2021	A	`conf`	Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? AISTATS Chaoqi Wang, Shengyang Sun, Roger B. Grosse
2021	A*	`conf`	Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. NeurIPS Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse
2021	J	`jnl`	Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. CoRR Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse
2021	J	`jnl`	Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. CoRR Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse
2021	A*	`conf`	LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. ICML Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, Christian Szegedy
2021	J	`jnl`	LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. CoRR Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, Christian Szegedy
2021	A*	`conf`	Learning Branching Heuristics for Propositional Model Counting. AAAI Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Sanjit A. Seshia, Fahiem Bacchus
2021	J	`jnl`	Learning to Give Checkable Answers with Prover-Verifier Games. CoRR Cem Anil, Guodong Zhang, Yuhuai Wu, Roger B. Grosse
2021	A*	`conf`	On Monotonic Linear Interpolation of Neural Network Parameters. ICML James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
2021	A*	`conf`	Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. ICML Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse
2021	J	`jnl`	Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. CoRR Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse
2021	A	`conf`	Understanding and Mitigating Exploding Inverses in Invertible Neural Networks. AISTATS Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen
2020	J	`jnl`	A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints. CoRR Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger B. Grosse
2020	J	`jnl`	Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? CoRR Chaoqi Wang, Shengyang Sun, Roger B. Grosse
2020	A*	`conf`	Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians. NeurIPS Juhan Bae, Roger B. Grosse
2020	J	`jnl`	Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians. CoRR Juhan Bae, Roger B. Grosse
2020	A*	`conf`	Evaluating Lossy Compression Rates of Deep Generative Models. ICML Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse
2020	J	`jnl`	Evaluating Lossy Compression Rates of Deep Generative Models. CoRR Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse
2020	J	`jnl`	INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving. CoRR Yuhuai Wu, Albert Q. Jiang, Jimmy Ba, Roger B. Grosse
2020	J	`jnl`	Learning Branching Heuristics for Propositional Model Counting. CoRR Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Edward A. Lee, Sanjit A. Seshia, Fahiem Bacchus
2020	A*	`conf`	Picking Winning Tickets Before Training by Preserving Gradient Flow. ICLR Chaoqi Wang, Guodong Zhang, Roger B. Grosse
2020	A*	`conf`	Regularized linear autoencoders recover the principal components, eventually. NeurIPS Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse
2020	J	`jnl`	Regularized linear autoencoders recover the principal components, eventually. CoRR Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse
2020	J	`jnl`	The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning. CoRR Yuhuai Wu, Honghua Dong, Roger B. Grosse, Jimmy Ba
2020	J	`jnl`	Understanding and mitigating exploding inverses in invertible neural networks. CoRR Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen
2020	J	`jnl`	When Does Preconditioning Help or Hurt Generalization? CoRR Shun-ichi Amari, Jimmy Ba, Roger B. Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
2019	—	`conf`	Aggregated Momentum: Stability Through Passive Damping. ICLR (Poster) James Lucas, Shengyang Sun, Richard S. Zemel, Roger B. Grosse
2019	A*	`conf`	Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. NeurIPS James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
2019	J	`jnl`	Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. CoRR James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
2019	A*	`conf`	EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. ICML Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang
2019	J	`jnl`	EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. CoRR Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang
2019	A*	`conf`	Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks. NeurIPS Guodong Zhang, James Martens, Roger B. Grosse
2019	J	`jnl`	Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks. CoRR Guodong Zhang, James Martens, Roger B. Grosse
2019	J	`jnl`	Functional Variational Bayesian Neural Networks. CoRR Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger B. Grosse
2019	—	`conf`	Functional variational Bayesian Neural Networks. ICLR (Poster) Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger B. Grosse
2019	A*	`conf`	Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. NeurIPS Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen
2019	J	`jnl`	Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. CoRR Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen
2019	—	`conf`	Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. ICLR (Poster) Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
2019	J	`jnl`	Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. CoRR Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
2019	A*	`conf`	Sorting Out Lipschitz Function Approximation. ICML Cem Anil, James Lucas, Roger B. Grosse
2019	—	`conf`	Three Mechanisms of Weight Decay Regularization. ICLR (Poster) Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse
2019	—	`conf`	TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer. ICLR (Poster) Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse
2019	—	`conf`	Understanding Posterior Collapse in Generative Latent Variable Models. DGS@ICLR James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
2019	A*	`conf`	Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. NeurIPS Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse
2019	J	`jnl`	Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. CoRR Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse
2018	J	`jnl`	A Coordinate-Free Construction of Scalable Natural Gradient. CoRR Kevin Luk, Roger B. Grosse
2018	A*	`conf`	Adversarial Distillation of Bayesian Neural Network Posteriors. ICML Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel
2018	J	`jnl`	Adversarial Distillation of Bayesian Neural Network Posteriors. CoRR Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel
2018	J	`jnl`	Aggregated Momentum: Stability Through Passive Damping. CoRR James Lucas, Richard S. Zemel, Roger B. Grosse
2018	A*	`conf`	Differentiable Compositional Kernel Learning for Gaussian Processes. ICML Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse
2018	J	`jnl`	Differentiable Compositional Kernel Learning for Gaussian Processes. CoRR Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse
2018	J	`jnl`	Eigenvalue Corrected Noisy Natural Gradient. CoRR Juhan Bae, Guodong Zhang, Roger B. Grosse
2018	—	`conf`	Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. ICLR (Poster) Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
2018	J	`jnl`	Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. CoRR Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
2018	—	`conf`	Isolating Sources of Disentanglement in Variational Autoencoders. ICLR (Workshop) Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
2018	A*	`conf`	Isolating Sources of Disentanglement in Variational Autoencoders. NeurIPS Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
2018	J	`jnl`	Isolating Sources of Disentanglement in Variational Autoencoders. CoRR Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
2018	A*	`conf`	Noisy Natural Gradient as Variational Inference. ICML Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse
2018	A*	`conf`	Reversible Recurrent Neural Networks. NeurIPS Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse
2018	J	`jnl`	Reversible Recurrent Neural Networks. CoRR Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse
2018	J	`jnl`	Sorting out Lipschitz function approximation. CoRR Cem Anil, James Lucas, Roger B. Grosse
2018	—	`conf`	Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. ICLR (Workshop) Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens
2018	J	`jnl`	Three Mechanisms of Weight Decay Regularization. CoRR Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse
2018	J	`jnl`	TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer. CoRR Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse
2018	—	`conf`	Understanding Short-Horizon Bias in Stochastic Meta-Optimization. ICLR (Poster) Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse
2018	J	`jnl`	Understanding Short-Horizon Bias in Stochastic Meta-Optimization. CoRR Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse
2017	A	`conf`	Discovering and Exploiting Additive Structure for Bayesian Optimization. AISTATS Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse
2017	—	`conf`	Distributed Second-Order Optimization using Kronecker-Factored Approximations. ICLR (Poster) Jimmy Ba, Roger B. Grosse, James Martens
2017	J	`jnl`	Noisy Natural Gradient as Variational Inference. CoRR Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse
2017	—	`conf`	On the Quantitative Analysis of Decoder-Based Generative Models. ICLR (Poster) Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse
2017	—	`conf`	Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation NIPS Yuhuai Wu, Elman Mansimov, Roger B. Grosse, Shun Liao, Jimmy Ba
2017	J	`jnl`	Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. CoRR Yuhuai Wu, Elman Mansimov, Shun Liao, Roger B. Grosse, Jimmy Ba
2017	—	`conf`	The Reversible Residual Network: Backpropagation Without Storing Activations. NIPS Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse
2017	J	`jnl`	The Reversible Residual Network: Backpropagation Without Storing Activations. CoRR Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse
2016	A*	`conf`	A Kronecker-factored approximate Fisher matrix for convolution layers. ICML Roger B. Grosse, James Martens
2016	J	`jnl`	A Kronecker-factored approximate Fisher matrix for convolution layers. CoRR Roger B. Grosse, James Martens
2016	A*	`conf`	Importance Weighted Autoencoders. ICLR (Poster) Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov
2016	—	`conf`	Measuring the reliability of MCMC inference with bidirectional Monte Carlo. NIPS Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
2016	J	`jnl`	Measuring the reliability of MCMC inference with bidirectional Monte Carlo. CoRR Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
2016	J	`jnl`	On the Quantitative Analysis of Decoder-Based Generative Models. CoRR Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse
2015	A	`conf`	Accurate and conservative estimates of MRF log-likelihood using reverse annealing. AISTATS Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov
2015	—	`conf`	Learning Wake-Sleep Recurrent Attention Models. NIPS Jimmy Ba, Ruslan Salakhutdinov, Roger B. Grosse, Brendan J. Frey
2015	J	`jnl`	Learning Wake-Sleep Recurrent Attention Models. CoRR Jimmy Ba, Roger B. Grosse, Ruslan Salakhutdinov, Brendan J. Frey
2015	A*	`conf`	Optimizing Neural Networks with Kronecker-factored Approximate Curvature. ICML James Martens, Roger B. Grosse
2015	J	`jnl`	Optimizing Neural Networks with Kronecker-factored Approximate Curvature. CoRR James Martens, Roger B. Grosse
2015	J	`jnl`	Sandwiching the marginal likelihood using bidirectional Monte Carlo. CoRR Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams
2015	A*	`conf`	Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix. ICML Roger B. Grosse, Ruslan Salakhutdinov
2015	J	`jnl`	Statistical Inference, Learning and Models in Big Data. CoRR 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
2014	J	`jnl`	Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing. CoRR Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov
2014	A*	`conf`	Automatic Construction and Natural-Language Description of Nonparametric Regression Models. AAAI James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2014	J	`jnl`	Automatic Construction and Natural-Language Description of Nonparametric Regression Models. CoRR James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2014	J	`jnl`	Testing MCMC code. CoRR Roger B. Grosse, David Kristjanson Duvenaud
2013	—	`conf`	Annealing between distributions by averaging moments. NIPS Roger B. Grosse, Chris J. Maddison, Ruslan Salakhutdinov
2013	J	`jnl`	Structure Discovery in Nonparametric Regression through Compositional Kernel Search CoRR David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2013	—	`conf`	Structure Discovery in Nonparametric Regression through Compositional Kernel Search. ICML (3) David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2012	J	`jnl`	Exploiting compositionality to explore a large space of model structures CoRR Roger B. Grosse, Ruslan Salakhutdinov, William T. Freeman, Joshua B. Tenenbaum
2012	A	`conf`	Exploiting compositionality to explore a large space of model structures. UAI Roger B. Grosse, Ruslan Salakhutdinov, William T. Freeman, Joshua B. Tenenbaum
2012	J	`jnl`	Shift-Invariance Sparse Coding for Audio Classification CoRR Roger B. Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng
2011	J	`jnl`	Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng
2009	A*	`conf`	Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. ICML Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng
2009	A*	`conf`	Ground truth dataset and baseline evaluations for intrinsic image algorithms. ICCV Roger B. Grosse, Micah K. Johnson, Edward H. Adelson, William T. Freeman
2007	A	`conf`	Shift-Invariance Sparse Coding for Audio Classification. UAI Roger B. Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng