Alexander Immer
19 papers · 2019–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (6) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (5) π Cross-Pollinator (15)
π
Conference Polyglot
(5)
π
Academic Marathon
(6)
π
Renaissance Researcher
(5)
π₯
Mega-Team
(25)
π
Triple Crown
π¬
Deep Specialist
(11)
π
Keyword Champion
(8)
π
Century Club
(19)
β‘
Prolific Year
(5)
π₯
Unstoppable
(7)
ποΈ
Keyword Collector
(67)
Conferences
NIPS (8)
ICML (6)
ICLR (3)
ACL (1)
AISTATS (1)
Top co-authors
Research topics
Keywords
laplace approximation
(8)
marginal likelihood
(5)
bayesian inference
(4)
gaussian process
(3)
hyperparameter optimization
(3)
neural network
(3)
neural tangent kernel
(2)
uncertainty quantification
(2)
neural network architecture
(2)
bayesian model selection
(2)
continual learning
(1)
approximate inference
(1)
variational inference
(1)
model selection
(1)
neural network pruning
(1)
neural network training
(1)
data augmentation
(1)
feature learning
(1)
representation learning
(1)
epistemic uncertainty
(1)
Papers
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
ICLR 2025
Influence Functions for Scalable Data Attribution in Diffusion Models
ICLR 2025
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
NIPS 2024
Improving Neural Additive Models with Bayesian Principles
ICML 2024
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
ICLR 2024
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
NIPS 2023
On the Identifiability and Estimation of Causal Location-Scale Noise Models
ICML 2023
Learning Layer-wise Equivariances Automatically using Gradients
NIPS 2023
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
NIPS 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
ICML 2023
Probing as Quantifying Inductive Bias
ACL 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
NIPS 2022
Improving predictions of Bayesian neural nets via local linearization
AISTATS 2021
Laplace Redux - Effortless Bayesian Deep Learning
NIPS 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
ICML 2021
Continual Deep Learning by Functional Regularisation of Memorable Past
NIPS 2020
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
ICML 2019
Approximate Inference Turns Deep Networks into Gaussian Processes
NIPS 2019