← Authors

Roger B Grosse

24 papers · 2013–2023 · 3 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (10) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (54) 🌍 Conference Polyglot (3) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (10) 🧭 Keyword Pioneer πŸ”₯ Unstoppable (9) ⚑ Prolific Year (5) πŸ’Ž Century Club (24) ❓ The Questioner (2) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (125)

Conferences

NIPS (19) ICML (4) AAAI (1)

Top co-authors

Jimmy Ba (4) Juhan Bae (4) Yuhuai Wu (4) Paul Vicol (3) James Lucas (3) Guodong Zhang (3) Cem Anil (2) Jakob Foerster (2) Sushant Sachdeva (2) Johannes Treutlein (2)

Research topics

Probability (1)

Keywords

neural network (4) stochastic gradient (3) annealed importance sampling (3) neural network optimization (2) bayesian inference (2) hyperparameter optimization (2) bilevel optimization (2) variational inference (2) convergence analysis (2) game theory (2) marginal likelihood (2) variational autoencoder (2) probabilistic inference (1) stochastic gradient descent (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 NIPS 2023
On Implicit Bias in Overparameterized Bilevel Optimization ICML 2022
Proximal Learning With Opponent-Learning Awareness NIPS 2022
If Influence Functions are the Answer, Then What is the Question? NIPS 2022
Amortized Proximal Optimization NIPS 2022
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation NIPS 2022
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NIPS 2021
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning ICML 2021
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition ICML 2021
On Monotonic Linear Interpolation of Neural Network Parameters ICML 2021
Learning Branching Heuristics for Propositional Model Counting AAAI 2021
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians NIPS 2020
Regularized linear autoencoders recover the principal components, eventually NIPS 2020
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model NIPS 2019
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse NIPS 2019
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks NIPS 2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks NIPS 2019
Isolating Sources of Disentanglement in Variational Autoencoders NIPS 2018
Reversible Recurrent Neural Networks NIPS 2018
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation NIPS 2017
The Reversible Residual Network: Backpropagation Without Storing Activations NIPS 2017
Measuring the reliability of MCMC inference with bidirectional Monte Carlo NIPS 2016
Learning Wake-Sleep Recurrent Attention Models NIPS 2015
Annealing between distributions by averaging moments NIPS 2013