Roger B Grosse
24 papers · 2013–2023 · 3 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+7 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (58) π Academic Marathon Β· Half π Conference Polyglot Β· Bronze π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π
Keyword Champion
ποΈ
Keyword Collector
(133)
π
Trend Setter
β‘
Prolific Year Β· Productive
π₯
Unstoppable Β· Bronze
β
The Questioner
(2)
π
Century Club Β· Bronze
Venues
Top co-authors
Research topics
Machine Learning
(23)
Optimization & Theory Β· Machine Learning
(19)
Deep Learning
(16)
Artificial Intelligence
(13)
Architectures
(8)
Core AI
(8)
Optimization & Theory Β· Deep Learning
(6)
Techniques
(6)
Learning Types Β· Machine Learning
(5)
Bayesian & Probabilistic Β· Artificial Intelligence
(4)
Bayesian & Probabilistic Β· Machine Learning
(4)
Core Methods
(4)
Learning Types Β· Deep Learning
(4)
Mathematics & Optimization
(3)
Reinforcement Learning
(2)
Keywords
neural network
(4)
convergence analysis
(3)
annealed importance sampling
(3)
stochastic gradient
(3)
neural network optimization
(3)
stochastic gradient descent
(2)
bayesian inference
(2)
hyperparameter optimization
(2)
game theory
(2)
bilevel optimization
(2)
variational inference
(2)
marginal likelihood
(2)
variational autoencoder
(2)
probabilistic inference
(1)
principal component analysis
(1)
image classification
(1)
image captioning
(1)
similarity-based reasoning
(1)
multi-agent reinforcement learning
(1)
kl divergence
(1)
Papers
Similarity-based cooperative equilibrium
NeurIPS 2023
Amortized Proximal Optimization
NeurIPS 2022
If Influence Functions are the Answer, Then What is the Question?
NeurIPS 2022
On Implicit Bias in Overparameterized Bilevel Optimization
ICML 2022
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
NeurIPS 2022
Proximal Learning With Opponent-Learning Awareness
NeurIPS 2022
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
NeurIPS 2021
Learning Branching Heuristics for Propositional Model Counting
AAAI 2021
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
ICML 2021
On Monotonic Linear Interpolation of Neural Network Parameters
ICML 2021
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
ICML 2021
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
NeurIPS 2020
Regularized linear autoencoders recover the principal components, eventually
NeurIPS 2020
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
NeurIPS 2019
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks
NeurIPS 2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
NeurIPS 2019
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
NeurIPS 2019
Isolating Sources of Disentanglement in Variational Autoencoders
NeurIPS 2018
Reversible Recurrent Neural Networks
NeurIPS 2018
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
NeurIPS 2017
The Reversible Residual Network: Backpropagation Without Storing Activations
NeurIPS 2017
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
NeurIPS 2016
Learning Wake-Sleep Recurrent Attention Models
NeurIPS 2015
Annealing between distributions by averaging moments
NeurIPS 2013