Alexander Immer
19 papers · 2019–2025 · 5 venues · across top CS/AI venues
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Machine Learning
(19)
Optimization & Theory · Machine Learning
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Deep Learning
(12)
Artificial Intelligence
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Bayesian & Probabilistic · Artificial Intelligence
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Bayesian & Probabilistic · Machine Learning
(4)
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Optimization & Theory · Deep Learning
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Keywords
laplace approximation
(8)
marginal likelihood
(6)
neural network
(4)
bayesian inference
(4)
gaussian process
(3)
hyperparameter optimization
(3)
neural tangent kernel
(2)
bayesian model selection
(2)
neural network architecture
(2)
bayesian deep learning
(2)
continual learning
(2)
uncertainty quantification
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approximate inference
(1)
model selection
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neural network training
(1)
feature learning
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representation learning
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active learning
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vision transformer
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feature selection
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Papers
Influence Functions for Scalable Data Attribution in Diffusion Models
ICLR 2025
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
ICLR 2025
Improving Neural Additive Models with Bayesian Principles
ICML 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
ICML 2024
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood
NeurIPS 2024
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
ICLR 2024
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
NeurIPS 2023
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
NeurIPS 2023
Learning Layer-wise Equivariances Automatically using Gradients
NeurIPS 2023
On the Identifiability and Estimation of Causal Location-Scale Noise Models
ICML 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
ICML 2023
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
NeurIPS 2022
Probing as Quantifying Inductive Bias
ACL 2022
Improving predictions of Bayesian neural nets via local linearization
AISTATS 2021
Laplace Redux - Effortless Bayesian Deep Learning
NeurIPS 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
ICML 2021
Continual Deep Learning by Functional Regularisation of Memorable Past
NeurIPS 2020
Approximate Inference Turns Deep Networks into Gaussian Processes
NeurIPS 2019
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
ICML 2019