Roger Grosse
24 papers · 2013–2024 · 6 venues · across top CS/AI venues
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Research topics
Machine Learning
(21)
Optimization & Theory
(17)
Deep Learning
(12)
Artificial Intelligence
(10)
Core AI
(6)
Learning Types
(6)
Models
(6)
Mathematics & Optimization
(5)
Optimization
(5)
Core Methods
(3)
Techniques
(3)
Application Areas
(2)
Bayesian & Probabilistic Β· Artificial Intelligence
(2)
Bayesian & Probabilistic Β· Machine Learning
(2)
Learning Paradigms
(2)
Keywords
bayesian neural network
(4)
natural gradient
(4)
gaussian process
(3)
second-order optimization
(3)
uncertainty estimation
(3)
bayesian optimization
(2)
fisher matrix
(2)
neural network
(2)
generative adversarial network
(2)
variational inference
(2)
convergence rate
(2)
kernel methods
(2)
kronecker factorization
(2)
gradient descent
(2)
model behavior
(2)
restricted boltzmann machine
(2)
function approximation
(1)
gaussian processes
(1)
adversarial robustness
(1)
bayesian inference
(1)
Papers
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
NeurIPS 2024
Training Data Attribution via Approximate Unrolling
NeurIPS 2024
Discovering Language Model Behaviors with Model-Written Evaluations
ACL 2023
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints
JMLR 2021
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
AISTATS 2021
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
AISTATS 2021
Evaluating Lossy Compression Rates of Deep Generative Models
ICML 2020
Picking Winning Tickets Before Training by Preserving Gradient Flow
ICLR 2020
Aggregated Momentum: Stability Through Passive Damping
ICLR 2019
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
ICML 2019
FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS
ICLR 2019
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
ICLR 2019
Sorting Out Lipschitz Function Approximation
ICML 2019
Three Mechanisms of Weight Decay Regularization
ICLR 2019
Adversarial Distillation of Bayesian Neural Network Posteriors
ICML 2018
Differentiable Compositional Kernel Learning for Gaussian Processes
ICML 2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
ICLR 2018
Noisy Natural Gradient as Variational Inference
ICML 2018
Discovering and Exploiting Additive Structure for Bayesian Optimization
AISTATS 2017
A Kronecker-factored approximate Fisher matrix for convolution layers
ICML 2016
Accurate and conservative estimates of MRF log-likelihood using reverse annealing
AISTATS 2015
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
ICML 2015
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix
ICML 2015
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
ICML 2013