Agustinus Kristiadi
Agustinus Kristiadi
Postdoc, Vector Institute
Verified email at - Homepage
Cited by
Cited by
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
A Kristiadi, M Hein, P Hennig
ICML 2020, 2020
Laplace Redux--Effortless Bayesian Deep Learning
E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ...
NeurIPS 2021, 2021
Incorporating literals into knowledge graph embeddings
A Kristiadi*, MA Khan*, D Lukovnikov, J Lehmann, A Fischer
ISWC 2019, 2019
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge
D Chaudhuri, A Kristiadi, J Lehmann, A Fischer
CoNLL 2018, 2018
Learnable Uncertainty under Laplace Approximations
A Kristiadi, M Hein, P Hennig
UAI 2021, 2021
Fast predictive uncertainty for classification with Bayesian deep networks
M Hobbhahn, A Kristiadi, P Hennig
UAI 2022, 2022
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
R Eschenhagen, E Daxberger, P Hennig, A Kristiadi
Bayesian Deep Learning Workshop, NeurIPS 2021, 2021
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence
A Kristiadi, M Hein, P Hennig
NeurIPS 2021, 2021
Predictive uncertainty quantification with compound density networks
A Kristiadi, S Däubener, A Fischer
Bayesian Deep Learning Workshop, NeurIPS 2019, 2019
Deep Convolutional Level Set Method for Image Segmentation.
A Kristiadi
Journal of ICT Research & Applications 11 (3), 2017
Being a Bit Frequentist Improves Bayesian Neural Networks
A Kristiadi, M Hein, P Hennig
AISTATS 2022, 2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
A Kristiadi, R Eschenhagen, P Hennig
NeurIPS 2022, 2022
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
A Kristiadi, A Immer, R Eschenhagen, V Fortuin
AABI 2023, 2023
Parallel particle swarm optimization for image segmentation
A Kristiadi, P Mudjihartono
Universitas Atma Jaya Yogyakarta, 2013
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
A Kristiadi, F Strieth-Kalthoff, M Skreta, P Poupart, A Aspuru-Guzik, ...
arXiv preprint arXiv:2402.05015, 2024
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
L Rendsburg, A Kristiadi, P Hennig, U von Luxburg
AISTATS 2022, 2022
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ...
arXiv preprint arXiv:2402.00809, 2024
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization
A Kristiadi, F Dangel, P Hennig
NeurIPS 2023, 2023
On the disconnect between theory and practice of overparametrized neural networks
J Wenger, F Dangel, A Kristiadi
arXiv preprint arXiv:2310.00137, 2023
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks
A Rashid, S Hacker, G Zhang, A Kristiadi, P Poupart
AISTATS 2024, 2024
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